Arxiv今日(2024-05-07)最新论文

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Abstract:We present a zero-shot pose optimization method that enforces accurate physical contact constraints when estimating the 3D pose of humans. Our central insight is that since language is often used to describe physical interaction, large pretrained text-based models can act as priors on pose estimation. We can thus leverage this insight to improve pose estimation by converting natural language descriptors, generated by a large multimodal model (LMM), into tractable losses to constrain the 3D pose optimization. Despite its simplicity, our method produces surprisingly compelling pose reconstructions of people in close contact, correctly capturing the semantics of the social and physical interactions. We demonstrate that our method rivals more complex state-of-the-art approaches that require expensive human annotation of contact points and training specialized models. Moreover, unlike previous approaches, our method provides a unified framework for resolving self-contact and person-to-person contact.

NLP-1-标题: Large Language Models Reveal Information Operation Goals Tactics and Narrative Frames

链接: https://arxiv.org/abs/2405.03688
作者: Keith Burghardt, Kai Chen, Kristina Lerman
备注: 15 pages, 9 figures

点击查看摘要

Abstract:Adversarial information operations can destabilize societies by undermining fair elections, manipulating public opinions on policies, and promoting scams. Despite their widespread occurrence and potential impacts, our understanding of influence campaigns is limited by manual analysis of messages and subjective interpretation of their observable behavior. In this paper, we explore whether these limitations can be mitigated with large language models (LLMs), using GPT-3.5 as a case-study for coordinated campaign annotation. We first use GPT-3.5 to scrutinize 126 identified information operations spanning over a decade. We utilize a number of metrics to quantify the close (if imperfect) agreement between LLM and ground truth descriptions. We next extract coordinated campaigns from two large multilingual datasets from X (formerly Twitter) that respectively discuss the 2022 French election and 2023 Balikaran Philippine-U.S. military exercise in 2023. For each coordinated campaign, we use GPT-3.5 to analyze posts related to a specific concern and extract goals, tactics, and narrative frames, both before and after critical events (such as the date of an election). While the GPT-3.5 sometimes disagrees with subjective interpretation, its ability to summarize and interpret demonstrates LLMs’ potential to extract higher-order indicators from text to provide a more complete picture of the information campaigns compared to previous methods.

NLP-2-标题: Language-Image Models with 3D Understanding

链接: https://arxiv.org/abs/2405.03685
作者: Jang Hyun Cho, Boris Ivanovic, Yulong Cao, Edward Schmerling, Yue Wang, Xinshuo Weng, Boyi Li, Yurong You, Philipp Krähenbühl, Yan Wang, Marco Pavone
备注: Project page: this https URL

点击查看摘要

Abstract:Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs’ perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we first develop a large-scale pre-training dataset for 2D and 3D called LV3D by combining multiple existing 2D and 3D recognition datasets under a common task formulation: as multi-turn question-answering. Next, we introduce a new MLLM named Cube-LLM and pre-train it on LV3D. We show that pure data scaling makes a strong 3D perception capability without 3D specific architectural design or training objective. Cube-LLM exhibits intriguing properties similar to LLMs: (1) Cube-LLM can apply chain-of-thought prompting to improve 3D understanding from 2D context information. (2) Cube-LLM can follow complex and diverse instructions and adapt to versatile input and output formats. (3) Cube-LLM can be visually prompted such as 2D box or a set of candidate 3D boxes from specialists. Our experiments on outdoor benchmarks demonstrate that Cube-LLM significantly outperforms existing baselines by 21.3 points of AP-BEV on the Talk2Car dataset for 3D grounded reasoning and 17.7 points on the DriveLM dataset for complex reasoning about driving scenarios, respectively. Cube-LLM also shows competitive results in general MLLM benchmarks such as refCOCO for 2D grounding with (87.0) average score, as well as visual question answering benchmarks such as VQAv2, GQA, SQA, POPE, etc. for complex reasoning. Our project is available at this https URL.

NLP-3-标题: Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis

链接: https://arxiv.org/abs/2405.03677
作者: Clayton Cohn, Caitlin Snyder, Justin Montenegro, Gautam Biswas
备注: In press at the 25th international conference on Artificial Intelligence in Education (AIED) Late-Breaking Results (LBR) track

点击查看摘要

Abstract:LLMs have demonstrated proficiency in contextualizing their outputs using human input, often matching or beating human-level performance on a variety of tasks. However, LLMs have not yet been used to characterize synergistic learning in students’ collaborative discourse. In this exploratory work, we take a first step towards adopting a human-in-the-loop prompt engineering approach with GPT-4-Turbo to summarize and categorize students’ synergistic learning during collaborative discourse. Our preliminary findings suggest GPT-4-Turbo may be able to characterize students’ synergistic learning in a manner comparable to humans and that our approach warrants further investigation.

NLP-4-标题: GREEN: Generative Radiology Report Evaluation and Error Notation

链接: https://arxiv.org/abs/2405.03595
作者: Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, Jean-Benoit Delbrouck
备注:

点击查看摘要

Abstract:Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images. Existing automatic evaluation metrics either suffer from failing to consider factual correctness (e.g., BLEU and ROUGE) or are limited in their interpretability (e.g., F1CheXpert and F1RadGraph). In this paper, we introduce GREEN (Generative Radiology Report Evaluation and Error Notation), a radiology report generation metric that leverages the natural language understanding of language models to identify and explain clinically significant errors in candidate reports, both quantitatively and qualitatively. Compared to current metrics, GREEN offers: 1) a score aligned with expert preferences, 2) human interpretable explanations of clinically significant errors, enabling feedback loops with end-users, and 3) a lightweight open-source method that reaches the performance of commercial counterparts. We validate our GREEN metric by comparing it to GPT-4, as well as to error counts of 6 experts and preferences of 2 experts. Our method demonstrates not only higher correlation with expert error counts, but simultaneously higher alignment with expert preferences when compared to previous approaches."

NLP-5-标题: Enabling High-Sparsity Foundational Llama Models with Efficient Pretrain ing and Deployment

链接: https://arxiv.org/abs/2405.03594
作者: Abhinav Agarwalla, Abhay Gupta, Alexandre Marques, Shubhra Pandit, Michael Goin, Eldar Kurtic, Kevin Leong, Tuan Nguyen, Mahmoud Salem, Dan Alistarh, Sean Lie, Mark Kurtz
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点击查看摘要

Abstract:Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accurate, sparse foundational versions of performant LLMs that achieve full accuracy recovery for fine-tuning tasks at up to 70% sparsity. We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset. We exhibit training acceleration due to sparsity on Cerebras CS-3 chips that closely matches theoretical scaling. In addition, we establish inference acceleration of up to 3x on CPUs by utilizing Neural Magic’s DeepSparse engine and 1.7x on GPUs through Neural Magic’s nm-vllm engine. The above gains are realized via sparsity alone, thus enabling further gains through additional use of quantization. Specifically, we show a total speedup on CPUs for sparse-quantized LLaMA models of up to 8.6x. We demonstrate these results across diverse, challenging tasks, including chat, instruction following, code generation, arithmetic reasoning, and summarization to prove their generality. This work paves the way for rapidly creating smaller and faster LLMs without sacrificing accuracy.

NLP-6-标题: AlphaMath Almost Zero: process Supervision without process

链接: https://arxiv.org/abs/2405.03553
作者: Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan
备注: Work in progress

点击查看摘要

Abstract:Recent advancements in large language models (LLMs) have substantially enhanced their mathematical reasoning abilities. However, these models still struggle with complex problems that require multiple reasoning steps, frequently leading to logical or numerical errors. While numerical mistakes can largely be addressed by integrating a code interpreter, identifying logical errors within intermediate steps is more challenging. Moreover, manually annotating these steps for training is not only expensive but also demands specialized expertise. In this study, we introduce an innovative approach that eliminates the need for manual annotation by leveraging the Monte Carlo Tree Search (MCTS) framework to generate both the process supervision and evaluation signals automatically. Essentially, when a LLM is well pre-trained, only the mathematical questions and their final answers are required to generate our training data, without requiring the solutions. We proceed to train a step-level value model designed to improve the LLM’s inference process in mathematical domains. Our experiments indicate that using automatically generated solutions by LLMs enhanced with MCTS significantly improves the model’s proficiency in dealing with intricate mathematical reasoning tasks.

NLP-7-标题: MAmmoTH2: Scaling Instructions from the Web

链接: https://arxiv.org/abs/2405.03548
作者: Xiang Yue, Tuney Zheng, Ge Zhang, Wenhu Chen
备注: Work in Progress

点击查看摘要

Abstract:Instruction tuning improves the reasoning abilities of large language models (LLMs), with data quality and scalability being the crucial factors. Most instruction tuning data come from human crowd-sourcing or GPT-4 distillation. We propose a paradigm to efficiently harvest 10 million naturally existing instruction data from the pre-training web corpus to enhance LLM reasoning. Our approach involves (1) recalling relevant documents, (2) extracting instruction-response pairs, and (3) refining the extracted pairs using open-source LLMs. Fine-tuning base LLMs on this dataset, we build MAmmoTH2 models, which significantly boost performance on reasoning benchmarks. Notably, MAmmoTH2-7B’s (Mistral) performance increases from 11% to 34% on MATH and from 36% to 67% on GSM8K without training on any in-domain data. Further training MAmmoTH2 on public instruction tuning datasets yields MAmmoTH2-Plus, achieving state-of-the-art performance on several reasoning and chatbot benchmarks. Our work demonstrates how to harvest large-scale, high-quality instruction data without costly human annotation or GPT-4 distillation, providing a new paradigm for building better instruction tuning data.

NLP-8-标题: Large Language Models ( LLM s) as Agent s for Augmented Democracy

链接: https://arxiv.org/abs/2405.03452
作者: Jairo Gudiño-Rosero, Umberto Grandi, César A. Hidalgo
备注: 15 pages main manuscript with 3 figures. 12 pages of supplementary material

点击查看摘要

Abstract:We explore the capabilities of an augmented democracy system built on off-the-shelf LLMs fine-tuned on data summarizing individual preferences across 67 policy proposals collected during the 2022 Brazilian presidential elections. We use a train-test cross-validation setup to estimate the accuracy with which the LLMs predict both: a subject’s individual political choices and the aggregate preferences of the full sample of participants. At the individual level, the accuracy of the out of sample predictions lie in the range 69%-76% and are significantly better at predicting the preferences of liberal and college educated participants. At the population level, we aggregate preferences using an adaptation of the Borda score and compare the ranking of policy proposals obtained from a probabilistic sample of participants and from data augmented using LLMs. We find that the augmented data predicts the preferences of the full population of participants better than probabilistic samples alone when these represent less than 30% to 40% of the total population. These results indicate that LLMs are potentially useful for the construction of systems of augmented democracy.

NLP-9-标题: Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models

链接: https://arxiv.org/abs/2405.03425
作者: Emre Onal, Klemens Flöge, Emma Caldwell, Arsen Sheverdin, Vincent Fortuin
备注: 14 pages, 1 figure, 2 tables

点击查看摘要

Abstract:Fine-tuned Large Language Models (LLMs) often suffer from overconfidence and poor calibration, particularly when fine-tuned on small datasets. To address these challenges, we propose a simple combination of Low-Rank Adaptation (LoRA) with Gaussian Stochastic Weight Averaging (SWAG), facilitating approximate Bayesian inference in LLMs. Through extensive testing across several Natural Language Processing (NLP) benchmarks, we demonstrate that our straightforward and computationally efficient approach improves model generalization and calibration. We further show that our method exhibits greater robustness against distribution shift, as reflected in its performance on out-of-distribution tasks.

NLP-10-标题: The high dimensional psychological profile and cultural bias of ChatGPT

链接: https://arxiv.org/abs/2405.03387
作者: Hang Yuan (1), Zhongyue Che (1), Shao Li (1), Yue Zhang, Xiaomeng Hu (2), Siyang Luo (1) ((1) Sun Yat-Sen University, (2) Renmin University of China)
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点击查看摘要

Abstract:Given the rapid advancement of large-scale language models, artificial intelligence (AI) models, like ChatGPT, are playing an increasingly prominent role in human society. However, to ensure that artificial intelligence models benefit human society, we must first fully understand the similarities and differences between the human-like characteristics exhibited by artificial intelligence models and real humans, as well as the cultural stereotypes and biases that artificial intelligence models may exhibit in the process of interacting with humans. This study first measured ChatGPT in 84 dimensions of psychological characteristics, revealing differences between ChatGPT and human norms in most dimensions as well as in high-dimensional psychological representations. Additionally, through the measurement of ChatGPT in 13 dimensions of cultural values, it was revealed that ChatGPT’s cultural value patterns are dissimilar to those of various countries/regions worldwide. Finally, an analysis of ChatGPT’s performance in eight decision-making tasks involving interactions with humans from different countries/regions revealed that ChatGPT exhibits clear cultural stereotypes in most decision-making tasks and shows significant cultural bias in third-party punishment and ultimatum games. The findings indicate that, compared to humans, ChatGPT exhibits a distinct psychological profile and cultural value orientation, and it also shows cultural biases and stereotypes in interpersonal decision-making. Future research endeavors should emphasize enhanced technical oversight and augmented transparency in the database and algorithmic training procedures to foster more efficient cross-cultural communication and mitigate social disparities.

NLP-11-标题: Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom WWW’2024

链接: https://arxiv.org/abs/2405.03371
作者: Bo Wang, Jing Ma, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian, Yi Chang
备注: 12 pages, WWW’2024

点击查看摘要

Abstract:Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justification. Existing explainable systems generate veracity justifications from investigative journalism, which suffer from debunking delayed and low efficiency. Recent studies simply assume that the justification is equivalent to the majority opinions expressed in the wisdom of crowds. However, the opinions typically contain some inaccurate or biased information since the wisdom of crowds is uncensored. To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework. Specifically, we first propose an evidence extraction module to split the wisdom of crowds into two competing parties and respectively detect salient evidences. To gain concise insights from evidences, we then design a prompt-based module that utilizes a large language model to generate justifications by inferring reasons towards two possible veracities. Finally, we propose a defense-based inference module to determine veracity via modeling the defense among these justifications. Extensive experiments conducted on two real-world benchmarks demonstrate that our proposed method outperforms state-of-the-art baselines in terms of fake news detection and provides high-quality justifications.

NLP-12-标题: MedDoc-Bot: A Chat Tool for Comparative Analysis of Large Language Models in the Context of the Pediatric Hypertension Guideline

链接: https://arxiv.org/abs/2405.03359
作者: Mohamed Yaseen Jabarulla, Steffen Oeltze-Jafra, Philipp Beerbaum, Theodor Uden
备注: {copyright} 2024 IEEE. This work has been accepted for publication and presentation at the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, to be held in Orlando, Florida, USA, July 15-19, 2024

点击查看摘要

Abstract:This research focuses on evaluating the non-commercial open-source large language models (LLMs) Meditron, MedAlpaca, Mistral, and Llama-2 for their efficacy in interpreting medical guidelines saved in PDF format. As a specific test scenario, we applied these models to the guidelines for hypertension in children and adolescents provided by the European Society of Cardiology (ESC). Leveraging Streamlit, a Python library, we developed a user-friendly medical document chatbot tool (MedDoc-Bot). This tool enables authorized users to upload PDF files and pose questions, generating interpretive responses from four locally stored LLMs. A pediatric expert provides a benchmark for evaluation by formulating questions and responses extracted from the ESC guidelines. The expert rates the model-generated responses based on their fidelity and relevance. Additionally, we evaluated the METEOR and chrF metric scores to assess the similarity of model responses to reference answers. Our study found that Llama-2 and Mistral performed well in metrics evaluation. However, Llama-2 was slower when dealing with text and tabular data. In our human evaluation, we observed that responses created by Mistral, Meditron, and Llama-2 exhibited reasonable fidelity and relevance. This study provides valuable insights into the strengths and limitations of LLMs for future developments in medical document interpretation. Open-Source Code: this https URL

NLP-13-标题: Lifelong Knowledge Editing for LLM s with Retrieval-Augmented Continuous Prompt Learning

链接: https://arxiv.org/abs/2405.03279
作者: Qizhou Chen, Taolin Zhang, Dongyang Li, Longtao Huang, Hui Xue, Chengyu Wang, Xiaofeng He
备注: 14 pages, 4 figures, 6 tables

点击查看摘要

Abstract:Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging task that caters to the continuous editing requirements of LLMs. Prior works primarily focus on single or batch editing; nevertheless, these methods fall short in lifelong editing scenarios due to catastrophic knowledge forgetting and the degradation of model performance. Although retrieval-based methods alleviate these issues, they are impeded by slow and cumbersome processes of integrating the retrieved knowledge into the model. In this work, we introduce RECIPE, a RetriEval-augmented ContInuous Prompt lEarning method, to boost editing efficacy and inference efficiency in lifelong learning. RECIPE first converts knowledge statements into short and informative continuous prompts, prefixed to the LLM’s input query embedding, to efficiently refine the response grounded on the knowledge. It further integrates the Knowledge Sentinel (KS) that acts as an intermediary to calculate a dynamic threshold, determining whether the retrieval repository contains relevant knowledge. Our retriever and prompt encoder are jointly trained to achieve editing properties, i.e., reliability, generality, and locality. In our experiments, RECIPE is assessed extensively across multiple LLMs and editing datasets, where it achieves superior editing performance. RECIPE also demonstrates its capability to maintain the overall performance of LLMs alongside showcasing fast editing and inference speed.

NLP-14-标题: A Philosophical Introduction to Language Models - Part II: The Way Forward

链接: https://arxiv.org/abs/2405.03207
作者: Raphaël Millière, Cameron Buckner
备注:

点击查看摘要

Abstract:In this paper, the second of two companion pieces, we explore novel philosophical questions raised by recent progress in large language models (LLMs) that go beyond the classical debates covered in the first part. We focus particularly on issues related to interpretability, examining evidence from causal intervention methods about the nature of LLMs’ internal representations and computations. We also discuss the implications of multimodal and modular extensions of LLMs, recent debates about whether such systems may meet minimal criteria for consciousness, and concerns about secrecy and reproducibility in LLM research. Finally, we discuss whether LLM-like systems may be relevant to modeling aspects of human cognition, if their architectural characteristics and learning scenario are adequately constrained.

NLP-15-标题: Vietnamese AI Generated Text Detection

链接: https://arxiv.org/abs/2405.03206
作者: Quang-Dan Tran, Van-Quan Nguyen, Quang-Huy Pham, K. B. Thang Nguyen, Trong-Hop Do
备注:

点击查看摘要

Abstract:In recent years, Large Language Models (LLMs) have become integrated into our daily lives, serving as invaluable assistants in completing tasks. Widely embraced by users, the abuse of LLMs is inevitable, particularly in using them to generate text content for various purposes, leading to difficulties in distinguishing between text generated by LLMs and that written by humans. In this study, we present a dataset named ViDetect, comprising 6.800 samples of Vietnamese essay, with 3.400 samples authored by humans and the remainder generated by LLMs, serving the purpose of detecting text generated by AI. We conducted evaluations using state-of-the-art methods, including ViT5, BartPho, PhoBERT, mDeberta V3, and mBERT. These results contribute not only to the growing body of research on detecting text generated by AI but also demonstrate the adaptability and effectiveness of different methods in the Vietnamese language context. This research lays the foundation for future advancements in AI-generated text detection and provides valuable insights for researchers in the field of natural language processing.

NLP-16-标题: Anchored Answers: Unravelling Positional Bias in GPT -2s Multiple-Choice Questions

链接: https://arxiv.org/abs/2405.03205
作者: Ruizhe Li, Yanjun Gao
备注: Work in process

点击查看摘要

Abstract:Large Language Models (LLMs), such as the GPT-4 and LLaMA families, have demonstrated considerable success across diverse tasks, including multiple-choice questions (MCQs). However, these models exhibit a positional bias, particularly an even worse anchored bias in the GPT-2 family, where they consistently favour the first choice ‘A’ in MCQs during inference. This anchored bias challenges the integrity of GPT-2’s decision-making process, as it skews performance based on the position rather than the content of the choices in MCQs. In this study, we utilise the mechanistic interpretability approach to identify the internal modules within GPT-2 models responsible for this bias. We focus on the Multi-Layer Perceptron (MLP) layers and attention heads, using the “logit lens” method to trace and modify the specific value vectors that contribute to the bias. By updating these vectors within MLP and recalibrating attention patterns to neutralise the preference for the first choice ‘A’, we effectively mitigate the anchored bias. Our interventions not only correct the bias but also improve the overall MCQ prediction accuracy for the GPT-2 family across various datasets. This work represents the first comprehensive mechanistic analysis of anchored bias in MCQs within the GPT-2 models, introducing targeted, minimal-intervention strategies that significantly enhance GPT2 model robustness and accuracy in MCQs. Our code is available at this https URL.

NLP-17-标题: Oracle-Checker Scheme for Evaluating a Generative Large Language Model

链接: https://arxiv.org/abs/2405.03170
作者: Yueling Jenny Zeng, Li-C. Wang, Thomas Ibbetson
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点击查看摘要

Abstract:This work presents a novel approach called oracle-checker scheme for evaluating the answer given by a generative large language model (LLM). Two types of checkers are presented. The first type of checker follows the idea of property testing. The second type of checker follows the idea of program checking. Their applications are demonstrated in two separate contexts, entity extraction and paraphrase decision, respectively.

NLP-18-标题: Advancing Multimodal Medical Capabilities of Gemini

链接: https://arxiv.org/abs/2405.03162
作者: Lin Yang, Shawn Xu, Andrew Sellergren, Timo Kohlberger, Yuchen Zhou, Ira Ktena, Atilla Kiraly, Faruk Ahmed, Farhad Hormozdiari, Tiam Jaroensri, Eric Wang, Ellery Wulczyn, Fayaz Jamil, Theo Guidroz, Chuck Lau, Siyuan Qiao, Yun Liu, Akshay Goel, Kendall Park, Arnav Agharwal, Nick George, Yang Wang, Ryutaro Tanno, David G. T. Barrett, Wei-Hung Weng, S. Sara Mahdavi, Khaled Saab, Tao Tu, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Jorge Cuadros, Gregory Sorensen, Yossi Matias, Katherine Chou, Greg Corrado, Joelle Barral, Shravya Shetty, David Fleet, S. M. Ali Eslami, Daniel Tse, Shruthi Prabhakara, Cory McLean, Dave Steiner, Rory Pilgrim, Christopher Kelly, Shekoofeh Azizi, Daniel Golden
备注:

点击查看摘要

Abstract:Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Building upon Gemini’s multimodal models, we develop several models within the new Med-Gemini family that inherit core capabilities of Gemini and are optimized for medical use via fine-tuning with 2D and 3D radiology, histopathology, ophthalmology, dermatology and genomic data. Med-Gemini-2D sets a new standard for AI-based chest X-ray (CXR) report generation based on expert evaluation, exceeding previous best results across two separate datasets by an absolute margin of 1% and 12%, where 57% and 96% of AI reports on normal cases, and 43% and 65% on abnormal cases, are evaluated as “equivalent or better” than the original radiologists’ reports. We demonstrate the first ever large multimodal model-based report generation for 3D computed tomography (CT) volumes using Med-Gemini-3D, with 53% of AI reports considered clinically acceptable, although additional research is needed to meet expert radiologist reporting quality. Beyond report generation, Med-Gemini-2D surpasses the previous best performance in CXR visual question answering (VQA) and performs well in CXR classification and radiology VQA, exceeding SoTA or baselines on 17 of 20 tasks. In histopathology, ophthalmology, and dermatology image classification, Med-Gemini-2D surpasses baselines across 18 out of 20 tasks and approaches task-specific model performance. Beyond imaging, Med-Gemini-Polygenic outperforms the standard linear polygenic risk score-based approach for disease risk prediction and generalizes to genetically correlated diseases for which it has never been trained. Although further development and evaluation are necessary in the safety-critical medical domain, our results highlight the potential of Med-Gemini across a wide range of medical tasks.

NLP-19-标题: Exploring the Potential of the Large Language Models ( LLM s) in Identifying Misleading News Headlines

链接: https://arxiv.org/abs/2405.03153
作者: Md Main Uddin Rony, Md Mahfuzul Haque, Mohammad Ali, Ahmed Shatil Alam, Naeemul Hassan
备注: 5 pages, 2 tables, 1st HEAL Workshop at CHI Conference on Human Factors in Computing Systems, May 12, Honolulu, HI, USA 2024

点击查看摘要

Abstract:In the digital age, the prevalence of misleading news headlines poses a significant challenge to information integrity, necessitating robust detection mechanisms. This study explores the efficacy of Large Language Models (LLMs) in identifying misleading versus non-misleading news headlines. Utilizing a dataset of 60 articles, sourced from both reputable and questionable outlets across health, science & tech, and business domains, we employ three LLMs- ChatGPT-3.5, ChatGPT-4, and Gemini-for classification. Our analysis reveals significant variance in model performance, with ChatGPT-4 demonstrating superior accuracy, especially in cases with unanimous annotator agreement on misleading headlines. The study emphasizes the importance of human-centered evaluation in developing LLMs that can navigate the complexities of misinformation detection, aligning technical proficiency with nuanced human judgment. Our findings contribute to the discourse on AI ethics, emphasizing the need for models that are not only technically advanced but also ethically aligned and sensitive to the subtleties of human interpretation.

NLP-20-标题: Quantifying the Capabilities of LLM s across Scale and Precision

链接: https://arxiv.org/abs/2405.03146
作者: Sher Badshah, Hassan Sajjad
备注:

点击查看摘要

Abstract:Scale is often attributed as one of the factors that cause an increase in the performance of LLMs, resulting in models with billion and trillion parameters. One of the limitations of such large models is the high computational requirements that limit their usage, deployment, and debugging in resource-constrained scenarios. Two commonly used alternatives to bypass these limitations are to use the smaller versions of LLMs (e.g. Llama 7B instead of Llama 70B) and lower the memory requirements by using quantization. While these approaches effectively address the limitation of resources, their impact on model performance needs thorough examination. In this study, we perform a comprehensive evaluation to investigate the effect of model scale and quantization on the performance. We experiment with two major families of open-source instruct models ranging from 7 billion to 70 billion parameters. Our extensive zero-shot experiments across various tasks including natural language understanding, reasoning, misinformation detection, and hallucination reveal that larger models generally outperform their smaller counterparts, suggesting that scale remains an important factor in enhancing performance. We found that larger models show exceptional resilience to precision reduction and can maintain high accuracy even at 4-bit quantization for numerous tasks and they serve as a better solution than using smaller models at high precision under similar memory requirements.

NLP-21-标题: CRAFT: Extracting and Tuning Cultural Instructions from the Wild

链接: https://arxiv.org/abs/2405.03138
作者: Bin Wang, Geyu Lin, Zhengyuan Liu, Chengwei Wei, Nancy F. Chen
备注: 6 pages

点击查看摘要

Abstract:Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications. Despite their wide use cases, their understanding of culturally-related concepts and reasoning remains limited. Meantime, there is a significant need to enhance these models’ cultural reasoning capabilities, especially concerning underrepresented regions. This paper introduces a novel pipeline for extracting high-quality, culturally-related instruction tuning datasets from vast unstructured corpora. We utilize a self-instruction generation pipeline to identify cultural concepts and trigger instruction. By integrating with a general-purpose instruction tuning dataset, our model demonstrates enhanced capabilities in recognizing and understanding regional cultural nuances, thereby enhancing its reasoning capabilities. We conduct experiments across three regions: Singapore, the Philippines, and the United States, achieving performance improvement of up to 6%. Our research opens new avenues for extracting cultural instruction tuning sets directly from unstructured data, setting a precedent for future innovations in the field.

NLP-22-标题: Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training

链接: https://arxiv.org/abs/2405.03133
作者: Zexuan Zhong, Mengzhou Xia, Danqi Chen, Mike Lewis
备注: 21 pages, 12 figures

点击查看摘要

Abstract:Mixture-of-experts (MoE) models facilitate efficient scaling; however, training the router network introduces the challenge of optimizing a non-differentiable, discrete objective. Recently, a fully-differentiable MoE architecture, SMEAR, was proposed (Muqeeth et al., 2023), which softly merges experts in the parameter space; nevertheless, its effectiveness was only demonstrated in downstream fine-tuning on classification tasks. In this paper, we present Lory, the first approach that scales such architectures to autoregressive language model pre-training. Lory introduces two key techniques: (1) a causal segment routing strategy that achieves high efficiency for expert merging operations while preserving the autoregressive nature of language models; (2) a similarity-based data batching method that encourages expert specialization by grouping similar documents in training instances. We pre-train a series of Lory models on 150B tokens from scratch, with up to 32 experts and 30B (1.5B active) parameters. Experimental results show significant performance gains over parameter-matched dense models on both perplexity (+13.9%) and a variety of downstream tasks (+1.5%-11.1%). Despite segment-level routing, Lory models achieve competitive performance compared to state-of-the-art MoE models with token-level routing. We further demonstrate that the trained experts in Lory capture domain-level specialization without supervision. Our work highlights the potential of fully-differentiable MoE architectures for language model pre-training and advocates future research in this area.

NLP-23-标题: An Active Inference Agent for Simulating Human Translation Processes in a Hierarchical Architecture: Integrating the Task Segment Framework and the HOF taxonomy

链接: https://arxiv.org/abs/2405.03111
作者: Michael Carl
备注:

点击查看摘要

Abstract:In this paper, we propose modelling human translation production as a hierarchy of three embedded translation processes. The proposed architecture replicates the temporal dynamics of keystroke production across sensorimotor, cognitive, and phenomenal layers. Utilizing data from the CRITT TPR-DB, the Task Segment Framework, and the HOF taxonomy, we demonstrate the temporal breakdown of the typing flow on distinct timelines within these three layers.

NLP-24-标题: FairMonitor: A Dual-framework for Detecting Stereotypes and Biases in Large Language Models

链接: https://arxiv.org/abs/2405.03098
作者: Yanhong Bai, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xingjiao Wu, Liang He
备注:

点击查看摘要

Abstract:Detecting stereotypes and biases in Large Language Models (LLMs) is crucial for enhancing fairness and reducing adverse impacts on individuals or groups when these models are applied. Traditional methods, which rely on embedding spaces or are based on probability metrics, fall short in revealing the nuanced and implicit biases present in various contexts. To address this challenge, we propose the FairMonitor framework and adopt a static-dynamic detection method for a comprehensive evaluation of stereotypes and biases in LLMs. The static component consists of a direct inquiry test, an implicit association test, and an unknown situation test, including 10,262 open-ended questions with 9 sensitive factors and 26 educational scenarios. And it is effective for evaluating both explicit and implicit biases. Moreover, we utilize the multi-agent system to construst the dynamic scenarios for detecting subtle biases in more complex and realistic setting. This component detects the biases based on the interaction behaviors of LLMs across 600 varied educational scenarios. The experimental results show that the cooperation of static and dynamic methods can detect more stereotypes and biased in LLMs.

NLP-25-标题: To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models ICML2024

链接: https://arxiv.org/abs/2405.03097
作者: George-Octavian Barbulescu, Peter Triantafillou
备注: Published as a conference paper at ICML 2024

点击查看摘要

Abstract:LLMs have been found to memorize training textual sequences and regurgitate verbatim said sequences during text generation time. This fact is known to be the cause of privacy and related (e.g., copyright) problems. Unlearning in LLMs then takes the form of devising new algorithms that will properly deal with these side-effects of memorized data, while not hurting the model’s utility. We offer a fresh perspective towards this goal, namely, that each textual sequence to be forgotten should be treated differently when being unlearned based on its degree of memorization within the LLM. We contribute a new metric for measuring unlearning quality, an adversarial attack showing that SOTA algorithms lacking this perspective fail for privacy, and two new unlearning methods based on Gradient Ascent and Task Arithmetic, respectively. A comprehensive performance evaluation across an extensive suite of NLP tasks then mapped the solution space, identifying the best solutions under different scales in model capacities and forget set sizes and quantified the gains of the new approaches.

NLP-26-标题: Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation

链接: https://arxiv.org/abs/2405.03085
作者: Kaize Shi, Xueyao Sun, Qing Li, Guandong Xu
备注:

点击查看摘要

Abstract:Large Language Models (LLMs) have made significant strides in information acquisition. However, their overreliance on potentially flawed parametric knowledge leads to hallucinations and inaccuracies, particularly when handling long-tail, domain-specific queries. Retrieval Augmented Generation (RAG) addresses this limitation by incorporating external, non-parametric knowledge. Nevertheless, the retrieved long-context documents often contain noisy, irrelevant information alongside vital knowledge, negatively diluting LLMs’ attention. Inspired by the supportive role of essential concepts in individuals’ reading comprehension, we propose a novel concept-based RAG framework with the Abstract Meaning Representation (AMR)-based concept distillation algorithm. The proposed algorithm compresses the cluttered raw retrieved documents into a compact set of crucial concepts distilled from the informative nodes of AMR by referring to reliable linguistic features. The concepts explicitly constrain LLMs to focus solely on vital information in the inference process. We conduct extensive experiments on open-domain question-answering datasets to empirically evaluate the proposed method’s effectiveness. The results indicate that the concept-based RAG framework outperforms other baseline methods, particularly as the number of supporting documents increases, while also exhibiting robustness across various backbone LLMs. This emphasizes the distilled concepts are informative for augmenting the RAG process by filtering out interference information. To the best of our knowledge, this is the first work introducing AMR to enhance the RAG, presenting a potential solution to augment inference performance with semantic-based context compression.

NLP-27-标题: Analyzing Emotional Trends from X platform using SenticNet: A Comparative Analysis with Cryptocurrency Price

链接: https://arxiv.org/abs/2405.03084
作者: Moein Shahiki Tash, Zahra Ahani, Olga Kolesnikova, Grigori Sidorov
备注:

点击查看摘要

Abstract:This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023. Leveraging SenticNet, we identified emotions like Fear and Anxiety, Rage and Anger, Grief and Sadness, Delight and Pleasantness, Enthusiasm and Eagerness, and Delight and Joy. Following data extraction, we segmented each month into bi-weekly intervals, replicating this process for price data obtained from Finance-Yahoo. Consequently, a comparative analysis was conducted, establishing connections between emotional trends observed across bi-weekly intervals and cryptocurrency prices, uncovering significant correlations between emotional sentiments and coin valuations.

NLP-28-标题: On the performativity of SDG classifications in large bibliometric databases

链接: https://arxiv.org/abs/2405.03007
作者: Matteo Ottaviani, Stephan Stahlschmidt
备注:

点击查看摘要

Abstract:Large bibliometric databases, such as Web of Science, Scopus, and OpenAlex, facilitate bibliometric analyses, but are performative, affecting the visibility of scientific outputs and the impact measurement of participating entities. Recently, these databases have taken up the UN’s Sustainable Development Goals (SDGs) in their respective classifications, which have been criticised for their diverging nature. This work proposes using the feature of large language models (LLMs) to learn about the “data bias” injected by diverse SDG classifications into bibliometric data by exploring five SDGs. We build a LLM that is fine-tuned in parallel by the diverse SDG classifications inscribed into the databases’ SDG classifications. Our results show high sensitivity in model architecture, classified publications, fine-tuning process, and natural language generation. The wide arbitrariness at different levels raises concerns about using LLM in research practice.

NLP-29-标题: Exploring prompt s to elicit memorization in masked language model-based named entity recognition

链接: https://arxiv.org/abs/2405.03004
作者: Yuxi Xia, Anastasiia Sedova, Pedro Henrique Luz de Araujo, Vasiliki Kougia, Lisa Nußbaumer, Benjamin Roth
备注:

点击查看摘要

Abstract:Training data memorization in language models impacts model capability (generalization) and safety (privacy risk). This paper focuses on analyzing prompts’ impact on detecting the memorization of 6 masked language model-based named entity recognition models. Specifically, we employ a diverse set of 400 automatically generated prompts, and a pairwise dataset where each pair consists of one person’s name from the training set and another name out of the set. A prompt completed with a person’s name serves as input for getting the model’s confidence in predicting this name. Finally, the prompt performance of detecting model memorization is quantified by the percentage of name pairs for which the model has higher confidence for the name from the training set. We show that the performance of different prompts varies by as much as 16 percentage points on the same model, and prompt engineering further increases the gap. Moreover, our experiments demonstrate that prompt performance is model-dependent but does generalize across different name sets. A comprehensive analysis indicates how prompt performance is influenced by prompt properties, contained tokens, and the model’s self-attention weights on the prompt.

NLP-30-标题: Parameter-Efficient Fine-Tuning with Discrete Fourier Transform ICML2024

链接: https://arxiv.org/abs/2405.03003
作者: Ziqi Gao, Qichao Wang, Aochuan Chen, Zijing Liu, Bingzhe Wu, Liang Chen, Jia Li
备注: Accepted by ICML 2024

点击查看摘要

Abstract:Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank matrices A and B to represent the weight change, i.e., \Delta W=BA . Despite LoRA’s progress, it faces storage challenges when handling extensive customization adaptations or larger base models. In this work, we aim to further compress trainable parameters by enjoying the powerful expressiveness of the Fourier transform. Specifically, we introduce FourierFT, which treats \Delta W as a matrix in the spatial domain and learns only a small fraction of its spectral coefficients. With the trained spectral coefficients, we implement the inverse discrete Fourier transform to recover \Delta W . Empirically, our FourierFT method shows comparable or better performance with fewer parameters than LoRA on various tasks, including natural language understanding, natural language generation, instruction tuning, and image classification. For example, when performing instruction tuning on the LLaMA2-7B model, FourierFT surpasses LoRA with only 0.064M trainable parameters, compared to LoRA’s 33.5M. Our code is released at \urlthis https URL.

NLP-31-标题: MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning

链接: https://arxiv.org/abs/2405.03000
作者: Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Hang Wu, Carl Yang, May D. Wang
备注: Work in Progress

点击查看摘要

Abstract:Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and corporate privacy. In this work, we propose MedAdapter, a unified post-hoc adapter for test-time adaptation of LLMs towards biomedical applications. Instead of fine-tuning the entire LLM, MedAdapter effectively adapts the original model by fine-tuning only a small BERT-sized adapter to rank candidate solutions generated by LLMs. Experiments demonstrate that MedAdapter effectively adapts both white-box and black-box LLMs in biomedical reasoning, achieving average performance improvements of 25.48% and 11.31%, respectively, without requiring extensive computational resources or sharing data with third parties. MedAdapter also yields superior performance when combined with train-time adaptation, highlighting a flexible and complementary solution to existing adaptation methods. Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain.

NLP-32-标题: Can Large Language Models Make the Grade? An Empirical Study Evaluating LLM s Ability to Mark Short Answer Questions in K-12 Education

链接: https://arxiv.org/abs/2405.02985
作者: Owen Henkel, Adam Boxer, Libby Hills, Bill Roberts
备注:

点击查看摘要

Abstract:This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open text responses to short answer questions, Specifically, we explore how well different combinations of GPT version and prompt engineering strategies performed at marking real student answers to short answer across different domain areas (Science and History) and grade-levels (spanning ages 5-16) using a new, never-used-before dataset from Carousel, a quizzing platform. We found that GPT-4, with basic few-shot prompting performed well (Kappa, 0.70) and, importantly, very close to human-level performance (0.75). This research builds on prior findings that GPT-4 could reliably score short answer reading comprehension questions at a performance-level very close to that of expert human raters. The proximity to human-level performance, across a variety of subjects and grade levels suggests that LLMs could be a valuable tool for supporting low-stakes formative assessment tasks in K-12 education and has important implications for real-world education delivery.

NLP-33-标题: E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods

链接: https://arxiv.org/abs/2405.02984
作者: Şükrü Öztürk, Hacer Yalim Keles
备注: 7 pages, 3 figures, 4 tables, submitted to IEEE conference

点击查看摘要

Abstract:This study introduces the continuous Educational Turkish Sign Language (E-TSL) dataset, collected from online Turkish language lessons for 5th, 6th, and 8th grades. The dataset comprises 1,410 videos totaling nearly 24 hours and includes performances from 11 signers. Turkish, an agglutinative language, poses unique challenges for sign language translation, particularly with a vocabulary where 64% are singleton words and 85% are rare words, appearing less than five times. We developed two baseline models to address these challenges: the Pose to Text Transformer (P2T-T) and the Graph Neural Network based Transformer (GNN-T) models. The GNN-T model achieved 19.13% BLEU-1 score and 3.28% BLEU-4 score, presenting a significant challenge compared to existing benchmarks. The P2T-T model, while demonstrating slightly lower performance in BLEU scores, achieved a higher ROUGE-L score of 22.09%. Additionally, we benchmarked our model using the well-known PHOENIX-Weather 2014T dataset to validate our approach.

NLP-34-标题: Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive Study

链接: https://arxiv.org/abs/2405.02937
作者: Fatema Tuj Johora Faria, Mukaffi Bin Moin, Asif Iftekher Fahim, Pronay Debnath, Faisal Muhammad Shah
备注: Accepted in 4th International Conference on Computing and Communication Networks (ICCCNet-2024)

点击查看摘要

Abstract:Natural Language Inference (NLI) is a cornerstone of Natural Language Processing (NLP), providing insights into the entailment relationships between text pairings. It is a critical component of Natural Language Understanding (NLU), demonstrating the ability to extract information from spoken or written interactions. NLI is mainly concerned with determining the entailment relationship between two statements, known as the premise and hypothesis. When the premise logically implies the hypothesis, the pair is labeled entailment''. If the hypothesis contradicts the premise, the pair receives the contradiction’’ label. When there is insufficient evidence to establish a connection, the pair is described as ``neutral’'. Despite the success of Large Language Models (LLMs) in various tasks, their effectiveness in NLI remains constrained by issues like low-resource domain accuracy, model overconfidence, and difficulty in capturing human judgment disagreements. This study addresses the underexplored area of evaluating LLMs in low-resourced languages such as Bengali. Through a comprehensive evaluation, we assess the performance of prominent LLMs and state-of-the-art (SOTA) models in Bengali NLP tasks, focusing on natural language inference. Utilizing the XNLI dataset, we conduct zero-shot and few-shot evaluations, comparing LLMs like GPT-3.5 Turbo and Gemini 1.5 Pro with models such as BanglaBERT, Bangla BERT Base, DistilBERT, mBERT, and sahajBERT. Our findings reveal that while LLMs can achieve comparable or superior performance to fine-tuned SOTA models in few-shot scenarios, further research is necessary to enhance our understanding of LLMs in languages with modest resources like Bengali. This study underscores the importance of continued efforts in exploring LLM capabilities across diverse linguistic contexts.

NLP-35-标题: Enabling Patient-side Disease Prediction via the Integration of Patient Narratives

链接: https://arxiv.org/abs/2405.02935
作者: Zhixiang Su, Yinan Zhang, Jiazheng Jing, Jie Xiao, Zhiqi Shen
备注:

点击查看摘要

Abstract:Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measures. However, most recent disease prediction approaches heavily rely on laboratory test outcomes (e.g., blood tests and medical imaging from X-rays). Gaining access to such data for precise disease prediction is often a complex task from the standpoint of a patient and is always only available post-patient consultation. To make disease prediction available from patient-side, we propose Personalized Medical Disease Prediction (PoMP), which predicts diseases using patient health narratives including textual descriptions and demographic information. By applying PoMP, patients can gain a clearer comprehension of their conditions, empowering them to directly seek appropriate medical specialists and thereby reducing the time spent navigating healthcare communication to locate suitable doctors. We conducted extensive experiments using real-world data from Haodf to showcase the effectiveness of PoMP.

NLP-36-标题: Relay Decoding: Concatenating Large Language Models for Machine Translation

链接: https://arxiv.org/abs/2405.02933
作者: Chengpeng Fu, Xiaocheng Feng, Yichong Huang, Wenshuai Huo, Baohang Li, Hui Wang, Bin Qin, Ting Liu
备注: Work in progress

点击查看摘要

Abstract:Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handling both the source and target languages in machine translation. When it is challenging to find large models that support the desired languages, resorting to continuous learning methods becomes a costly endeavor. To mitigate these expenses, we propose an innovative approach called RD (Relay Decoding), which entails concatenating two distinct large models that individually support the source and target languages. By incorporating a simple mapping layer to facilitate the connection between these two models and utilizing a limited amount of parallel data for training, we successfully achieve superior results in the machine translation task. Experimental results conducted on the Multi30k and WikiMatrix datasets validate the effectiveness of our proposed method.

NLP-37-标题: A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU LREC COLING2024

链接: https://arxiv.org/abs/2405.02925
作者: Guanhua Chen, Yutong Yao, Derek F. Wong, Lidia S. Chao
备注: LREC-COLING 2024

点击查看摘要

Abstract:Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous works train the model contrastively to increase the margin between different multi-intent labels, they are less suited to the nuances of multi-intent NLU. They ignore the rich information between the shared intents, which is beneficial to constructing a better embedding space, especially in low-data scenarios. We introduce a two-stage Prediction-Aware Contrastive Learning (PACL) framework for multi-intent NLU to harness this valuable knowledge. Our approach capitalizes on shared intent information by integrating word-level pre-training and prediction-aware contrastive fine-tuning. We construct a pre-training dataset using a word-level data augmentation strategy. Subsequently, our framework dynamically assigns roles to instances during contrastive fine-tuning while introducing a prediction-aware contrastive loss to maximize the impact of contrastive learning. We present experimental results and empirical analysis conducted on three widely used datasets, demonstrating that our method surpasses the performance of three prominent baselines on both low-data and full-data scenarios.

NLP-38-标题: Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models NAACL2024

链接: https://arxiv.org/abs/2405.02917
作者: Tobias Groot, Matias Valdenegro-Toro
备注: 8 pages, with appendix. To appear in TrustNLP workshop @ NAACL 2024

点击查看摘要

Abstract:Language and Vision-Language Models (LLMs/VLMs) have revolutionized the field of AI by their ability to generate human-like text and understand images, but ensuring their reliability is crucial. This paper aims to evaluate the ability of LLMs (GPT4, GPT-3.5, LLaMA2, and PaLM 2) and VLMs (GPT4V and Gemini Pro Vision) to estimate their verbalized uncertainty via prompting. We propose the new Japanese Uncertain Scenes (JUS) dataset, aimed at testing VLM capabilities via difficult queries and object counting, and the Net Calibration Error (NCE) to measure direction of miscalibration. Results show that both LLMs and VLMs have a high calibration error and are overconfident most of the time, indicating a poor capability for uncertainty estimation. Additionally we develop prompts for regression tasks, and we show that VLMs have poor calibration when producing mean/standard deviation and 95% confidence intervals.

NLP-39-标题: Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English

链接: https://arxiv.org/abs/2405.02887
作者: Aekansh Kathunia, Mohammad Kaif, Nalin Arora, N Narotam
备注: 6 pages, 3 Figures

点击查看摘要

Abstract:People communicate in more than 7,000 languages around the world, with around 780 languages spoken in India alone. Despite this linguistic diversity, research on Sentiment Analysis has predominantly focused on English text data, resulting in a disproportionate availability of sentiment resources for English. This paper examines the performance of transformer models in Sentiment Analysis tasks across multilingual datasets and text that has undergone machine translation. By comparing the effectiveness of these models in different linguistic contexts, we gain insights into their performance variations and potential implications for sentiment analysis across diverse languages. We also discuss the shortcomings and potential for future work towards the end.

NLP-40-标题: Revisiting a Pain in the Neck: Semantic Phrase Processing Benchmark for Language Models

链接: https://arxiv.org/abs/2405.02861
作者: Yang Liu, Melissa Xiaohui Qin, Hongming Li, Chao Huang
备注: 24 pages, 17 figures, 10 tables

点击查看摘要

Abstract:We introduce LexBench, a comprehensive evaluation suite enabled to test language models (LMs) on ten semantic phrase processing tasks. Unlike prior studies, it is the first work to propose a framework from the comparative perspective to model the general semantic phrase (i.e., lexical collocation) and three fine-grained semantic phrases, including idiomatic expression, noun compound, and verbal construction. Thanks to \ourbenchmark, we assess the performance of 15 LMs across model architectures and parameter scales in classification, extraction, and interpretation tasks. Through the experiments, we first validate the scaling law and find that, as expected, large models excel better than the smaller ones in most tasks. Second, we investigate further through the scaling semantic relation categorization and find that few-shot LMs still lag behind vanilla fine-tuned models in the task. Third, through human evaluation, we find that the performance of strong models is comparable to the human level regarding semantic phrase processing. Our benchmarking findings can serve future research aiming to improve the generic capability of LMs on semantic phrase comprehension. Our source code and data are available at this https URL

NLP-41-标题: Language Evolution for Evading Social Media Regulation via LLM -based Multi- agent Simulation

链接: https://arxiv.org/abs/2405.02858
作者: Jinyu Cai, Jialong Li, Mingyue Zhang, Munan Li, Chen-Shu Wang, Kenji Tei
备注: Accepted by IEEE WCCI 2024

点击查看摘要

Abstract:Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation framework using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework’s effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios.

NLP-42-标题: HuixiangDou-CR: Coreference Resolution in Group Chats

链接: https://arxiv.org/abs/2405.02817
作者: Huanjun Kong
备注: 5 pages, 3 tables, 3 figures

点击查看摘要

Abstract:How to eliminate pronominal reference in group chats? In this work, we have preprocessed 58k authentic chat data and manually annotated 2.3k questions. The reliability of this annotation was confirmed by the scaling law. After this, we conducted fine-tuning on Qwen models, ranging from 0.5B to 32B parameters. The optimal version improved 29.07 in F1 score. This confirms the viability of fine-tuning Large Language Model (LLM) for downstream Natural Language Processing (NLP) tasks. Our contributions are: 1) Created Supervised Fine-Tuning (SFT) training data in alpaca format, along with a set of Low-Rank Adaptation (LoRA) weights, and 2) Developed a method for acquiring high-quality data leveraging scaling law principle. The script, raw data with alpaca format and experiments track are open-sourced on Github this https URL, HuggingFace this https URL and WandB this https URL . The privacy of the data involved has been authorized by users.

NLP-43-标题: Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization SIGIR2024

链接: https://arxiv.org/abs/2405.02816
作者: Hamed Zamani, Michael Bendersky
备注: To appear in the proceedings of SIGIR 2024

点击查看摘要

Abstract:This paper introduces Stochastic RAG–a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work. Stochastic RAG casts the retrieval process in RAG as a stochastic sampling without replacement process. Through this formulation, we employ straight-through Gumbel-top-k that provides a differentiable approximation for sampling without replacement and enables effective end-to-end optimization for RAG. We conduct extensive experiments on seven diverse datasets on a wide range of tasks, from open-domain question answering to fact verification to slot-filling for relation extraction and to dialogue systems. By applying this optimization method to a recent and effective RAG model, we advance state-of-the-art results on six out of seven datasets.

NLP-44-标题: Negative Prompt : Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli IJCAI2024

链接: https://arxiv.org/abs/2405.02814
作者: Xu Wang, Cheng Li, Yi Chang, Jindong Wang, Yuan Wu
备注: This paper has been accepted by IJCAI 2024

点击查看摘要

Abstract:Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread adoption has spurred extensive research into LLMs across various disciplines, including the social sciences. Notably, studies have revealed that LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. We embark on rigorous experimental evaluations of five LLMs including Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks. The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12.89% in Instruction Induction tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention visualization experiments to decipher the underlying mechanisms of NegativePrompt’s influence. Our research contributes significantly to the understanding of LLMs and emotion interaction, demonstrating the practical efficacy of NegativePrompt as an emotion-driven method and offering novel insights for the enhancement of LLMs in real-world applications. The code is available at this https URL.

NLP-45-标题: ImageInWords: Unlocking Hyper-Detailed Image Descriptions

链接: https://arxiv.org/abs/2405.02793
作者: Roopal Garg, Andrea Burns, Burcu Karagol Ayan, Yonatan Bitton, Ceslee Montgomery, Yasumasa Onoe, Andrew Bunner, Ranjay Krishna, Jason Baldridge, Radu Soricut
备注: Webpage (this https URL), GitHub (this https URL), HuggingFace (this https URL)

点击查看摘要

Abstract:Despite the longstanding adage “an image is worth a thousand words,” creating accurate and hyper-detailed image descriptions for training Vision-Language models remains challenging. Current datasets typically have web-scraped descriptions that are short, low-granularity, and often contain details unrelated to the visual content. As a result, models trained on such data generate descriptions replete with missing information, visual inconsistencies, and hallucinations. To address these issues, we introduce ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process. We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness. Our dataset significantly improves across these dimensions compared to recently released datasets (+66%) and GPT-4V outputs (+48%). Furthermore, models fine-tuned with IIW data excel by +31% against prior work along the same human evaluation dimensions. Given our fine-tuned models, we also evaluate text-to-image generation and vision-language reasoning. Our model’s descriptions can generate images closest to the original, as judged by both automated and human metrics. We also find our model produces more compositionally rich descriptions, outperforming the best baseline by up to 6% on ARO, SVO-Probes, and Winoground datasets.

NLP-46-标题: Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLM s ICLR2024

链接: https://arxiv.org/abs/2405.02774
作者: Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
备注: Published as a conference paper at ICLR 2024

点击查看摘要

Abstract:This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms have been designed for small-scale applications, rendering them unsuitable for our context, some emerging methods do cater to language data scales. However, they often prioritize data that aligns with the target distribution. While this strategy may be effective when training a model from scratch, it can yield limited results when the model has already been pre-trained on a different distribution. Differing from prior work, our key idea is to select data that nudges the pre-training distribution closer to the target distribution. We show the optimality of this approach for fine-tuning tasks under certain conditions. We demonstrate the efficacy of our methodology across a diverse array of tasks (NLU, NLG, zero-shot) with models up to 2.7B, showing that it consistently surpasses other selection methods. Moreover, our proposed method is significantly faster than existing techniques, scaling to millions of samples within a single GPU hour. Our code is open-sourced (Code repository: https://anonymous.4open.science/r/DV4LLM-D761/ ). While fine-tuning offers significant potential for enhancing performance across diverse tasks, its associated costs often limit its widespread adoption; with this work, we hope to lay the groundwork for cost-effective fine-tuning, making its benefits more accessible.

NLP-47-标题: Detecting Edited Knowledge in Language Models

链接: https://arxiv.org/abs/2405.02765
作者: Paul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert
备注:

点击查看摘要

Abstract:Knowledge editing techniques (KEs) can update language models’ obsolete or inaccurate knowledge learned from pre-training. However, KE also faces potential malicious applications, e.g. inserting misinformation and toxic content. Moreover, in the context of responsible AI, it is instructive for end-users to know whether a generated output is driven by edited knowledge or first-hand knowledge from pre-training. To this end, we study detecting edited knowledge in language models by introducing a novel task: given an edited model and a specific piece of knowledge the model generates, our objective is to classify the knowledge as either “non-edited” (based on the pre-training), or ``edited’’ (based on subsequent editing). We initiate the task with two state-of-the-art KEs, two language models, and two datasets. We further propose a simple classifier, RepReg, a logistic regression model that takes hidden state representations as input features. Our results reveal that RepReg establishes a strong baseline, achieving a peak accuracy of 99.81%, and 97.79% in out-of-domain settings. Second, RepReg achieves near-optimal performance with a limited training set (200 training samples), and it maintains its performance even in out-of-domain settings. Last, we find it more challenging to separate edited and non-edited knowledge when they contain the same subject or object.

NLP-48-标题: Assessing Adversarial Robustness of Large Language Models : An Empirical Study

链接: https://arxiv.org/abs/2405.02764
作者: Zeyu Yang, Zhao Meng, Xiaochen Zheng, Roger Wattenhofer
备注: 16 pages, 9 figures, 10 tables

点击查看摘要

Abstract:Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We presents a novel white-box style attack approach that exposes vulnerabilities in leading open-source LLMs, including Llama, OPT, and T5. We assess the impact of model size, structure, and fine-tuning strategies on their resistance to adversarial perturbations. Our comprehensive evaluation across five diverse text classification tasks establishes a new benchmark for LLM robustness. The findings of this study have far-reaching implications for the reliable deployment of LLMs in real-world applications and contribute to the advancement of trustworthy AI systems.

NLP-49-标题: Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding NAACL2024

链接: https://arxiv.org/abs/2405.02750
作者: Zheng Zhao, Emilio Monti, Jens Lehmann, Haytham Assem
备注: Accepted to NAACL 2024

点击查看摘要

Abstract:Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially resulting in generated text with factual inconsistencies or contextually unfaithful content. LLMs utilize two primary knowledge sources: 1) prior (parametric) knowledge from pretraining, and 2) contextual (non-parametric) knowledge from input prompts. The study addresses the open question of how LLMs effectively balance these knowledge sources during the generation process, specifically in the context of open-domain question answering. To address this issue, we introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples to enhance robust context grounding during generation. Notably, our method operates at inference time without requiring further training. We conduct comprehensive experiments to demonstrate its applicability and effectiveness, providing empirical evidence showcasing its superiority over existing methodologies. Our code is publicly available at: this https URL.

NLP-50-标题: Beyond Performance: Quantifying and Mitigating Label Bias in LLM s NAACL2024

链接: https://arxiv.org/abs/2405.02743
作者: Yuval Reif, Roy Schwartz
备注: NAACL 2024

点击查看摘要

Abstract:Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However, recent work revealed they also exhibit label bias – an undesirable preference toward predicting certain answers over others. Still, detecting and measuring this bias reliably and at scale has remained relatively unexplored. In this study, we evaluate different approaches to quantifying label bias in a model’s predictions, conducting a comprehensive investigation across 279 classification tasks and ten LLMs. Our investigation reveals substantial label bias in models both before and after debiasing attempts, as well as highlights the importance of outcomes-based evaluation metrics, which were not previously used in this regard. We further propose a novel label bias calibration method tailored for few-shot prompting, which outperforms recent calibration approaches for both improving performance and mitigating label bias. Our results emphasize that label bias in the predictions of LLMs remains a barrier to their reliability.

NLP-51-标题: Relations Prediction for Knowledge Graph Completion using Large Language Models

链接: https://arxiv.org/abs/2405.02738
作者: Sakher Khalil Alqaaidi, Krzysztof Kochut
备注:

点击查看摘要

Abstract:Knowledge Graphs have been widely used to represent facts in a structured format. Due to their large scale applications, knowledge graphs suffer from being incomplete. The relation prediction task obtains knowledge graph completion by assigning one or more possible relations to each pair of nodes. In this work, we make use of the knowledge graph node names to fine-tune a large language model for the relation prediction task. By utilizing the node names only we enable our model to operate sufficiently in the inductive settings. Our experiments show that we accomplish new scores on a widely used knowledge graph benchmark.

NLP-52-标题: Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents

链接: https://arxiv.org/abs/2405.02732
作者: Sneha Singhania, Simon Razniewski, Gerhard Weikum
备注:

点击查看摘要

Abstract:Methods for relation extraction from text mostly focus on high precision, at the cost of limited recall. High recall is crucial, though, to populate long lists of object entities that stand in a specific relation with a given subject. Cues for relevant objects can be spread across many passages in long texts. This poses the challenge of extracting long lists from long texts. We present the L3X method which tackles the problem in two stages: (1) recall-oriented generation using a large language model (LLM) with judicious techniques for retrieval augmentation, and (2) precision-oriented scrutinization to validate or prune candidates. Our L3X method outperforms LLM-only generations by a substantial margin.

NLP-53-标题: Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness

链接: https://arxiv.org/abs/2405.02714
作者: Xinran Zhao, Tong Chen, Sihao Chen, Hongming Zhang, Tongshuang Wu
备注:

点击查看摘要

Abstract:The task of Information Retrieval (IR) requires a system to identify relevant documents based on users’ information needs. In real-world scenarios, retrievers are expected to not only rely on the semantic relevance between the documents and the queries but also recognize the nuanced intents or perspectives behind a user query. For example, when asked to verify a claim, a retrieval system is expected to identify evidence from both supporting vs. contradicting perspectives, for the downstream system to make a fair judgment call. In this work, we study whether retrievers can recognize and respond to different perspectives of the queries – beyond finding relevant documents for a claim, can retrievers distinguish supporting vs. opposing documents? We reform and extend six existing tasks to create a benchmark for retrieval, where we have diverse perspectives described in free-form text, besides root, neutral queries. We show that current retrievers covered in our experiments have limited awareness of subtly different perspectives in queries and can also be biased toward certain perspectives. Motivated by the observation, we further explore the potential to leverage geometric features of retriever representation space to improve the perspective awareness of retrievers in a zero-shot manner. We demonstrate the efficiency and effectiveness of our projection-based methods on the same set of tasks. Further analysis also shows how perspective awareness improves performance on various downstream tasks, with 4.2% higher accuracy on AmbigQA and 29.9% more correlation with designated viewpoints on essay writing, compared to non-perspective-aware baselines.

NLP-54-标题: CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions

链接: https://arxiv.org/abs/2405.02712
作者: Hanchong Zhang, Ruisheng Cao, Hongshen Xu, Lu Chen, Kai Yu
备注:

点击查看摘要

Abstract:Recently, Large Language Models (LLMs) have been demonstrated to possess impressive capabilities in a variety of domains and tasks. We investigate the issue of prompt design in the multi-turn text-to-SQL task and attempt to enhance the LLMs’ reasoning capacity when generating SQL queries. In the conversational context, the current SQL query can be modified from the preceding SQL query with only a few operations due to the context dependency. We introduce our method called CoE-SQL which can prompt LLMs to generate the SQL query based on the previously generated SQL query with an edition chain. We also conduct extensive ablation studies to determine the optimal configuration of our approach. Our approach outperforms different in-context learning baselines stably and achieves state-of-the-art performances on two benchmarks SParC and CoSQL using LLMs, which is also competitive to the SOTA fine-tuned models.

NLP-55-标题: Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning

链接: https://arxiv.org/abs/2405.02710
作者: Che Guan, Andrew Chin, Puya Vahabi
备注: 9 Pages

点击查看摘要

Abstract:With the deluge of information delivered by the daily news cycle, there is a growing need to effectively and efficiently summarize news feeds for quick consumption. We leverage large language models (LLMs), with their advanced learning and generative abilities as compared to conventional language models, to generate concise and coherent summaries for news articles from the XSum dataset. Our paper focuses on two key aspects of LLMs: Efficient in-context Learning (ELearn) and Parameter Efficient Fine-tuning (EFit). Under ELearn, we find that increasing the number of shots in prompts and utilizing simple templates generally improve the quality of summaries. We also find that utilizing relevant examples in few-shot learning for ELearn does not improve model performance. In addition, we studied EFit using different methods and demonstrate that fine-tuning the first layer of LLMs produces better outcomes as compared to fine-tuning other layers or utilizing LoRA. We also find that leveraging more relevant training samples using selective layers does not result in better performance. By combining ELearn and EFit, we create a new model (ELearnFit) that leverages the benefits of both few-shot learning and fine-tuning and produces superior performance to either model alone. We also use ELearnFit to highlight the trade-offs between prompting and fine-tuning, especially for situations where only a limited number of annotated samples are available. Ultimately, our research provides practical techniques to optimize news summarization during the prompting and fine-tuning stages and enhances the synthesis of news articles.

NLP-56-标题: Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing

链接: https://arxiv.org/abs/2405.02677
作者: Sebastián Concha Macías, Brian Keith Norambuena
备注: Text2Story Workshop 2024

点击查看摘要

Abstract:Narratives serve as fundamental frameworks in our understanding of the world and play a crucial role in collaborative sensemaking, providing a versatile foundation for sensemaking. Framing is a subtle yet potent mechanism that influences public perception through specific word choices, shaping interpretations of reported news events. Despite the recognized importance of narratives and framing, a significant gap exists in the literature with regard to the explicit consideration of framing within the context of computational extraction and representation. This article explores the capabilities of a specific narrative extraction and representation approach – narrative maps – to capture framing information from news data. The research addresses two key questions: (1) Does the narrative extraction method capture the framing distribution of the data set? (2) Does it produce a representation with consistent framing? Our results indicate that while the algorithm captures framing distributions, achieving consistent framing across various starting and ending events poses challenges. Our results highlight the potential of narrative maps to provide users with insights into the intricate framing dynamics within news narratives. However, we note that directly leveraging framing information in the computational narrative extraction process remains an open challenge.

NLP-57-标题: On the Information Redundancy in Non-Autoregressive Translation

链接: https://arxiv.org/abs/2405.02673
作者: Zhihao Wang, Longyue Wang, Jinsong Su, Junfeng Yao, Zhaopeng Tu
备注: 10 pages, 10 tables

点击查看摘要

Abstract:Token repetition is a typical form of multi-modal problem in fully non-autoregressive translation (NAT). In this work, we revisit the multi-modal problem in recently proposed NAT models. Our study reveals that these advanced models have introduced other types of information redundancy errors, which cannot be measured by the conventional metric - the continuous repetition ratio. By manually annotating the NAT outputs, we identify two types of information redundancy errors that correspond well to lexical and reordering multi-modality problems. Since human annotation is time-consuming and labor-intensive, we propose automatic metrics to evaluate the two types of redundant errors. Our metrics allow future studies to evaluate new methods and gain a more comprehensive understanding of their effectiveness.

NLP-58-标题: R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models

链接: https://arxiv.org/abs/2405.02659
作者: Taolin Zhang, Dongyang Li, Qizhou Chen, Chengyu Wang, Longtao Huang, Hui Xue, Xiaofeng He, Jun Huang
备注:

点击查看摘要

Abstract:Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the hallucination problem. However, existing retriever-responder methods typically append relevant documents to the prompt of LLMs to perform text generation tasks without considering the interaction of fine-grained structural semantics between the retrieved documents and the LLMs. This issue is particularly important for accurate response generation as LLMs tend to lose in the middle'' when dealing with input prompts augmented with lengthy documents. In this work, we propose a new pipeline named Reinforced Retriever-Reorder-Responder’’ (R ^4 ) to learn document orderings for retrieval-augmented LLMs, thereby further enhancing their generation abilities while the large numbers of parameters of LLMs remain frozen. The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement. Specifically, document order adjustment aims to organize retrieved document orderings into beginning, middle, and end positions based on graph attention learning, which maximizes the reinforced reward of response quality. Document representation enhancement further refines the representations of retrieved documents for responses of poor quality via document-level gradient adversarial learning. Extensive experiments demonstrate that our proposed pipeline achieves better factual question-answering performance on knowledge-intensive tasks compared to strong baselines across various public datasets. The source codes and trained models will be released upon paper acceptance.

NLP-59-标题: Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling LREC COLING2024

链接: https://arxiv.org/abs/2405.02650
作者: Maxim Ifergan, Renana Keydar, Omri Abend, Amit Pinchevski
备注: 9 pages, 7 figures, LREC-COLING 2024

点击查看摘要

Abstract:The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.

NLP-60-标题: TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants SIGIR2024

链接: https://arxiv.org/abs/2405.02637
作者: Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffery Dalton, Leif Azzopardi
备注: To appear in SIGIR 2024. arXiv admin note: substantial text overlap with arXiv:2401.01330

点击查看摘要

Abstract:Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and responding in a naturalistic manner to user requests. The extended TREC Interactive Knowledge Assistance Track (iKAT) collection aims to enable researchers to test and evaluate their Conversational Search Agents (CSA). The collection contains a set of 36 personalized dialogues over 20 different topics each coupled with a Personal Text Knowledge Base (PTKB) that defines the bespoke user personas. A total of 344 turns with approximately 26,000 passages are provided as assessments on relevance, as well as additional assessments on generated responses over four key dimensions: relevance, completeness, groundedness, and naturalness. The collection challenges CSA to efficiently navigate diverse personal contexts, elicit pertinent persona information, and employ context for relevant conversations. The integration of a PTKB and the emphasis on decisional search tasks contribute to the uniqueness of this test collection, making it an essential benchmark for advancing research in conversational and interactive knowledge assistants.

NLP-61-标题: Astro-NER – Astronomy Named Entity Recognition: Is GPT a Good Domain Expert Annotator?

链接: https://arxiv.org/abs/2405.02602
作者: Julia Evans, Sameer Sadruddin, Jennifer D’Souza
备注: 9 pages

点击查看摘要

Abstract:In this study, we address one of the challenges of developing NER models for scholarly domains, namely the scarcity of suitable labeled data. We experiment with an approach using predictions from a fine-tuned LLM model to aid non-domain experts in annotating scientific entities within astronomy literature, with the goal of uncovering whether such a collaborative process can approximate domain expertise. Our results reveal moderate agreement between a domain expert and the LLM-assisted non-experts, as well as fair agreement between the domain expert and the LLM model’s predictions. In an additional experiment, we compare the performance of finetuned and default LLMs on this task. We have also introduced a specialized scientific entity annotation scheme for astronomy, validated by a domain expert. Our approach adopts a scholarly research contribution-centric perspective, focusing exclusively on scientific entities relevant to the research theme. The resultant dataset, containing 5,000 annotated astronomy article titles, is made publicly available.

NLP-62-标题: Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning ICML2024

链接: https://arxiv.org/abs/2405.02596
作者: Jing Xu, Jingzhao Zhang
备注: ICML 2024

点击查看摘要

Abstract:Fine-tuning large language models (LLM) can be costly. Parameter-efficient fine-tuning (PEFT) addresses the problems by training a fraction of the parameters, whose success reveals the expressiveness and flexibility of pretrained models. This paper studies the limit of PEFT, by further simplifying its design and reducing the number of trainable parameters beyond standard setups. To this end, we use Random Masking to fine-tune the pretrained model. Despite its simplicity, we show that Random Masking is surprisingly effective: with a larger-than-expected learning rate, Random Masking can match the performance of standard PEFT algorithms such as LoRA on various tasks, using fewer trainable parameters. We provide both empirical and theoretical explorations into the success of Random Masking. We show that masking induces a flatter loss landscape and more distant solutions, which allows for and necessitates large learning rates.

NLP-63-标题: Mixat: A Data Set of Bilingual Emirati-English Speech

链接: https://arxiv.org/abs/2405.02578
作者: Maryam Al Ali, Hanan Aldarmaki
备注: SIGUL 2024

点击查看摘要

Abstract:This paper introduces Mixat: a dataset of Emirati speech code-mixed with English. Mixat was developed to address the shortcomings of current speech recognition resources when applied to Emirati speech, and in particular, to bilignual Emirati speakers who often mix and switch between their local dialect and English. The data set consists of 15 hours of speech derived from two public podcasts featuring native Emirati speakers, one of which is in the form of conversations between the host and a guest. Therefore, the collection contains examples of Emirati-English code-switching in both formal and natural conversational contexts. In this paper, we describe the process of data collection and annotation, and describe some of the features and statistics of the resulting data set. In addition, we evaluate the performance of pre-trained Arabic and multi-lingual ASR systems on our dataset, demonstrating the shortcomings of existing models on this low-resource dialectal Arabic, and the additional challenge of recognizing code-switching in ASR. The dataset will be made publicly available for research use.

NLP-64-标题: A Combination of BERT and Transformer for Vietnamese Spelling Correction

链接: https://arxiv.org/abs/2405.02573
作者: Hieu Ngo Trung, Duong Tran Ham, Tin Huynh, Kiem Hoang
备注: 13 pages

点击查看摘要

Abstract:Recently, many studies have shown the efficiency of using Bidirectional Encoder Representations from Transformers (BERT) in various Natural Language Processing (NLP) tasks. Specifically, English spelling correction task that uses Encoder-Decoder architecture and takes advantage of BERT has achieved state-of-the-art result. However, to our knowledge, there is no implementation in Vietnamese yet. Therefore, in this study, a combination of Transformer architecture (state-of-the-art for Encoder-Decoder model) and BERT was proposed to deal with Vietnamese spelling correction. The experiment results have shown that our model outperforms other approaches as well as the Google Docs Spell Checking tool, achieves an 86.24 BLEU score on this task.

NLP-65-标题: A Literature Review and Framework for Human Evaluation of Generative Large Language Models in Healthcare

链接: https://arxiv.org/abs/2405.02559
作者: Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R McCarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E. Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang
备注:

点击查看摘要

Abstract:As generative artificial intelligence (AI), particularly Large Language Models (LLMs), continues to permeate healthcare, it remains crucial to supplement traditional automated evaluations with human expert evaluation. Understanding and evaluating the generated texts is vital for ensuring safety, reliability, and effectiveness. However, the cumbersome, time-consuming, and non-standardized nature of human evaluation presents significant obstacles to the widespread adoption of LLMs in practice. This study reviews existing literature on human evaluation methodologies for LLMs within healthcare. We highlight a notable need for a standardized and consistent human evaluation approach. Our extensive literature search, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, spans publications from January 2018 to February 2024. This review provides a comprehensive overview of the human evaluation approaches used in diverse healthcare applications.This analysis examines the human evaluation of LLMs across various medical specialties, addressing factors such as evaluation dimensions, sample types, and sizes, the selection and recruitment of evaluators, frameworks and metrics, the evaluation process, and statistical analysis of the results. Drawing from diverse evaluation strategies highlighted in these studies, we propose a comprehensive and practical framework for human evaluation of generative LLMs, named QUEST: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence. This framework aims to improve the reliability, generalizability, and applicability of human evaluation of generative LLMs in different healthcare applications by defining clear evaluation dimensions and offering detailed guidelines.

NLP-66-标题: Mothman at SemEval-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization SEMEVAL-2024

链接: https://arxiv.org/abs/2405.02517
作者: Alvin Po-Chun Chen, Ray Groshan, Sean von Bayern
备注: 13 pages, 2 figures, to be published in Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

点击查看摘要

Abstract:Extensive research exists on the performance of large language models on logic-based tasks, whereas relatively little has been done on their ability to generate creative solutions on lateral thinking tasks. The BrainTeaser shared task tests lateral thinking and uses adversarial datasets to prevent memorization, resulting in poor performance for out-of-the-box models. We propose a system for iterative, chain-of-thought prompt engineering which optimizes prompts using human evaluation. Using this shared task, we demonstrate our system’s ability to significantly improve model performance by optimizing prompts and evaluate the input dataset.

NLP-67-标题: Beyond Helpfulness and Harmlessness: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning ICML2024

链接: https://arxiv.org/abs/2405.02501
作者: Hyeong Kyu Choi, Yixuan Li
备注: Paper accepted at ICML 2024

点击查看摘要

Abstract:Large Language Models (LLMs) are trained on massive text corpora, which are encoded with diverse personality traits. This triggers an interesting goal of eliciting a desired personality trait from the LLM, and probing its behavioral preferences. Accordingly, we formalize the persona elicitation task, aiming to customize LLM behaviors to align with a target persona. We present Persona In-Context Learning (PICLe), a novel persona elicitation framework grounded in Bayesian inference. At the core, PICLe introduces a new ICL example selection criterion based on likelihood ratio, which is designed to optimally guide the model in eliciting a specific target persona. We demonstrate the effectiveness of PICLe through extensive comparisons against baseline methods across three contemporary LLMs. Code is available at this https URL.

NLP-68-标题: Semantic Scaling: Bayesian Ideal Point Estimates with Large Language Models

链接: https://arxiv.org/abs/2405.02472
作者: Michael Burnham
备注:

点击查看摘要

Abstract:This paper introduces “Semantic Scaling,” a novel method for ideal point estimation from text. I leverage large language models to classify documents based on their expressed stances and extract survey-like data. I then use item response theory to scale subjects from these data. Semantic Scaling significantly improves on existing text-based scaling methods, and allows researchers to explicitly define the ideological dimensions they measure. This represents the first scaling approach that allows such flexibility outside of survey instruments and opens new avenues of inquiry for populations difficult to survey. Additionally, it works with documents of varying length, and produces valid estimates of both mass and elite ideology. I demonstrate that the method can differentiate between policy preferences and in-group/out-group affect. Among the public, Semantic Scaling out-preforms Tweetscores according to human judgement; in Congress, it recaptures the first dimension DW-NOMINATE while allowing for greater flexibility in resolving construct validity challenges.

NLP-69-标题: What is Sentiment Meant to Mean to Language Models?

链接: https://arxiv.org/abs/2405.02454
作者: Michael Burnham
备注:

点击查看摘要

Abstract:Sentiment analysis is one of the most widely used techniques in text analysis. Recent advancements with Large Language Models have made it more accurate and accessible than ever, allowing researchers to classify text with only a plain English prompt. However, “sentiment” entails a wide variety of concepts depending on the domain and tools used. It has been used to mean emotion, opinions, market movements, or simply a general ``good-bad’’ dimension. This raises a question: What exactly are language models doing when prompted to label documents by sentiment? This paper first overviews how sentiment is defined across different contexts, highlighting that it is a confounded measurement construct in that it entails multiple variables, such as emotional valence and opinion, without disentangling them. I then test three language models across two data sets with prompts requesting sentiment, valence, and stance classification. I find that sentiment labels most strongly correlate with valence labels. I further find that classification improves when researchers more precisely specify their dimension of interest rather than using the less well-defined concept of sentiment. I conclude by encouraging researchers to move beyond “sentiment” when feasible and use a more precise measurement construct.

NLP-70-标题: CALRec: Contrastive Alignment of Generative LLM s For Sequential Recommendation

链接: https://arxiv.org/abs/2405.02429
作者: Yaoyiran Li, Xiang Zhai, Moustafa Alzantot, Keyi Yu, Ivan Vulić, Anna Korhonen, Mohamed Hammad
备注:

点击查看摘要

Abstract:Traditional recommender systems such as matrix factorization methods rely on learning a shared dense embedding space to represent both items and user preferences. Sequence models such as RNN, GRUs, and, recently, Transformers have also excelled in the task of sequential recommendation. This task requires understanding the sequential structure present in users’ historical interactions to predict the next item they may like. Building upon the success of Large Language Models (LLMs) in a variety of tasks, researchers have recently explored using LLMs that are pretrained on vast corpora of text for sequential recommendation. To use LLMs in sequential recommendations, both the history of user interactions and the model’s prediction of the next item are expressed in text form. We propose CALRec, a two-stage LLM finetuning framework that finetunes a pretrained LLM in a two-tower fashion using a mixture of two contrastive losses and a language modeling loss: the LLM is first finetuned on a data mixture from multiple domains followed by another round of target domain finetuning. Our model significantly outperforms many state-of-the-art baselines (+37% in Recall@1 and +24% in NDCG@10) and systematic ablation studies reveal that (i) both stages of finetuning are crucial, and, when combined, we achieve improved performance, and (ii) contrastive alignment is effective among the target domains explored in our experiments.

NLP-71-标题: What does the Knowledge Neuron Thesis Have to do with Knowledge? ICLR2024

链接: https://arxiv.org/abs/2405.02421
作者: Jingcheng Niu, Andrew Liu, Zining Zhu, Gerald Penn
备注: ICLR 2024 (Spotlight)

点击查看摘要

Abstract:We reassess the Knowledge Neuron (KN) Thesis: an interpretation of the mechanism underlying the ability of large language models to recall facts from a training corpus. This nascent thesis proposes that facts are recalled from the training corpus through the MLP weights in a manner resembling key-value memory, implying in effect that “knowledge” is stored in the network. Furthermore, by modifying the MLP modules, one can control the language model’s generation of factual information. The plausibility of the KN thesis has been demonstrated by the success of KN-inspired model editing methods (Dai et al., 2022; Meng et al., 2022). We find that this thesis is, at best, an oversimplification. Not only have we found that we can edit the expression of certain linguistic phenomena using the same model editing methods but, through a more comprehensive evaluation, we have found that the KN thesis does not adequately explain the process of factual expression. While it is possible to argue that the MLP weights store complex patterns that are interpretable both syntactically and semantically, these patterns do not constitute “knowledge.” To gain a more comprehensive understanding of the knowledge representation process, we must look beyond the MLP weights and explore recent models’ complex layer structures and attention mechanisms.

NLP-72-标题: The Call for Socially Aware Language Technologies

链接: https://arxiv.org/abs/2405.02411
作者: Diyi Yang, Dirk Hovy, David Jurgens, Barbara Plank
备注:

点击查看摘要

Abstract:Language technologies have made enormous progress, especially with the introduction of large language models (LLMs). On traditional tasks such as machine translation and sentiment analysis, these models perform at near-human level. These advances can, however, exacerbate a variety of issues that models have traditionally struggled with, such as bias, evaluation, and risks. In this position paper, we argue that many of these issues share a common core: a lack of awareness of the factors, context, and implications of the social environment in which NLP operates, which we call social awareness. While NLP is getting better at solving the formal linguistic aspects, limited progress has been made in adding the social awareness required for language applications to work in all situations for all users. Integrating social awareness into NLP models will make applications more natural, helpful, and safe, and will open up new possibilities. Thus we argue that substantial challenges remain for NLP to develop social awareness and that we are just at the beginning of a new era for the field.

NLP-73-标题: LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model

链接: https://arxiv.org/abs/2405.02363
作者: Yulin Luo, Ruichuan An, Bocheng Zou, Yiming Tang, Jiaming Liu, Shanghang Zhang
备注:

点击查看摘要

Abstract:The distribution of subpopulations is an important property hidden within a dataset. Uncovering and analyzing the subpopulation distribution within datasets provides a comprehensive understanding of the datasets, standing as a powerful tool beneficial to various downstream tasks, including Dataset Subpopulation Organization, Subpopulation Shift, and Slice Discovery. Despite its importance, there has been no work that systematically explores the subpopulation distribution of datasets to our knowledge. To address the limitation and solve all the mentioned tasks in a unified way, we introduce a novel concept of subpopulation structures to represent, analyze, and utilize subpopulation distributions within datasets. To characterize the structures in an interpretable manner, we propose the Subpopulation Structure Discovery with Large Language Models (SSD-LLM) framework, which employs world knowledge and instruction-following capabilities of Large Language Models (LLMs) to linguistically analyze informative image captions and summarize the structures. Furthermore, we propose complete workflows to address downstream tasks, named Task-specific Tuning, showcasing the application of the discovered structure to a spectrum of subpopulation-related tasks, including dataset subpopulation organization, subpopulation shift, and slice discovery. Furthermore, we propose complete workflows to address downstream tasks, named Task-specific Tuning, showcasing the application of the discovered structure to a spectrum of subpopulation-related tasks, including dataset subpopulation organization, subpopulation shift, and slice discovery.

NLP-74-标题: Early Transformer s: A study on Efficient Training of Transformer Models through Early-Bird Lottery Tickets

链接: https://arxiv.org/abs/2405.02353
作者: Shravan Cheekati
备注:

点击查看摘要

Abstract:The training of Transformer models has revolutionized natural language processing and computer vision, but it remains a resource-intensive and time-consuming process. This paper investigates the applicability of the early-bird ticket hypothesis to optimize the training efficiency of Transformer models. We propose a methodology that combines iterative pruning, masked distance calculation, and selective retraining to identify early-bird tickets in various Transformer architectures, including ViT, Swin-T, GPT-2, and RoBERTa. Our experimental results demonstrate that early-bird tickets can be consistently found within the first few epochs of training or fine-tuning, enabling significant resource optimization without compromising performance. The pruned models obtained from early-bird tickets achieve comparable or even superior accuracy to their unpruned counterparts while substantially reducing memory usage. Furthermore, our comparative analysis highlights the generalizability of the early-bird ticket phenomenon across different Transformer models and tasks. This research contributes to the development of efficient training strategies for Transformer models, making them more accessible and resource-friendly. By leveraging early-bird tickets, practitioners can accelerate the progress of natural language processing and computer vision applications while reducing the computational burden associated with training Transformer models.

NLP-75-标题: COPAL: Continual Pruning in Large Language Generative Models ICML2024

链接: https://arxiv.org/abs/2405.02347
作者: Srikanth Malla, Joon Hee Choi, Chiho Choi
备注: Accepted to ICML2024

点击查看摘要

Abstract:Adapting pre-trained large language models to different domains in natural language processing requires two key considerations: high computational demands and model’s inability to continual adaptation. To simultaneously address both issues, this paper presents COPAL (COntinual Pruning in Adaptive Language settings), an algorithm developed for pruning large language generative models under a continual model adaptation setting. While avoiding resource-heavy finetuning or retraining, our pruning process is guided by the proposed sensitivity analysis. The sensitivity effectively measures model’s ability to withstand perturbations introduced by the new dataset and finds model’s weights that are relevant for all encountered datasets. As a result, COPAL allows seamless model adaptation to new domains while enhancing the resource efficiency. Our empirical evaluation on a various size of LLMs show that COPAL outperforms baseline models, demonstrating its efficacy in efficiency and adaptability.

NLP-76-标题: Speech Technology Services for Oral History Research

链接: https://arxiv.org/abs/2405.02333
作者: Christoph Draxler, Henk van den Heuvel, Arjan van Hessen, Pavel Ircing, Jan Lehečka
备注: 5 pages plus references, 3 figures

点击查看摘要

Abstract:Oral history is about oral sources of witnesses and commentors on historical events. Speech technology is an important instrument to process such recordings in order to obtain transcription and further enhancements to structure the oral account In this contribution we address the transcription portal and the webservices associated with speech processing at BAS, speech solutions developed at LINDAT, how to do it yourself with Whisper, remaining challenges, and future developments.

NLP-77-标题: Digital ASIC Design with Ongoing LLM s: Strategies and Prospects

链接: https://arxiv.org/abs/2405.02329
作者: Maoyang Xiang, Emil Goh, T. Hui Teo
备注: 8 pages, 2 figures, 1 table

点击查看摘要

Abstract:The escalating complexity of modern digital systems has imposed significant challenges on integrated circuit (IC) design, necessitating tools that can simplify the IC design flow. The advent of Large Language Models (LLMs) has been seen as a promising development, with the potential to automate the generation of Hardware Description Language (HDL) code, thereby streamlining digital IC design. However, the practical application of LLMs in this area faces substantial hurdles. Notably, current LLMs often generate HDL code with small but critical syntax errors and struggle to accurately convey the high-level semantics of circuit designs. These issues significantly undermine the utility of LLMs for IC design, leading to misinterpretations and inefficiencies. In response to these challenges, this paper presents targeted strategies to harness the capabilities of LLMs for digital ASIC design. We outline approaches that improve the reliability and accuracy of HDL code generation by LLMs. As a practical demonstration of these strategies, we detail the development of a simple three-phase Pulse Width Modulation (PWM) generator. This project, part of the “Efabless AI-Generated Open-Source Chip Design Challenge,” successfully passed the Design Rule Check (DRC) and was fabricated, showcasing the potential of LLMs to enhance digital ASIC design. This work underscores the feasibility and benefits of integrating LLMs into the IC design process, offering a novel approach to overcoming the complexities of modern digital systems.

NLP-78-标题: Evaluating LLM s for Hardware Design and Test

链接: https://arxiv.org/abs/2405.02326
作者: Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce
备注:

点击查看摘要

Abstract:Large Language Models (LLMs) have demonstrated capabilities for producing code in Hardware Description Languages (HDLs). However, most of the focus remains on their abilities to write functional code, not test code. The hardware design process consists of both design and test, and so eschewing validation and verification leaves considerable potential benefit unexplored, given that a design and test framework may allow for progress towards full automation of the digital design pipeline. In this work, we perform one of the first studies exploring how a LLM can both design and test hardware modules from provided specifications. Using a suite of 8 representative benchmarks, we examined the capabilities and limitations of the state-of-the-art conversational LLMs when producing Verilog for functional and verification purposes. We taped out the benchmarks on a Skywater 130nm shuttle and received the functional chip.

NLP-79-标题: NL2FOL: Translating Natural Language to First-Order Logic for Logical Fallacy Detection

链接: https://arxiv.org/abs/2405.02318
作者: Abhinav Lalwani, Lovish Chopra, Christopher Hahn, Caroline Trippel, Zhijing Jin, Mrinmaya Sachan
备注:

点击查看摘要

Abstract:Logical fallacies are common errors in reasoning that undermine the logic of an argument. Automatically detecting logical fallacies has important applications in tracking misinformation and validating claims. In this paper, we design a process to reliably detect logical fallacies by translating natural language to First-order Logic (FOL) step-by-step using Large Language Models (LLMs). We then utilize Satisfiability Modulo Theory (SMT) solvers to reason about the validity of the formula and classify inputs as either a fallacy or valid statement. Our model also provides a novel means of utilizing LLMs to interpret the output of the SMT solver, offering insights into the counter-examples that illustrate why a given sentence is considered a logical fallacy. Our approach is robust, interpretable and does not require training data or fine-tuning. We evaluate our model on a mixed dataset of fallacies and valid sentences. The results demonstrate improved performance compared to end-to-end LLMs, with our classifier achieving an F1-score of 71% on the Logic dataset. The approach is able to generalize effectively, achieving an F1-score of 73% on the challenge set, LogicClimate, outperforming state-of-the-art models by 21% despite its much smaller size.

NLP-80-标题: A geometric framework for interstellar discourse on fundamental physical structures

链接: https://arxiv.org/abs/2405.02314
作者: Giampiero Esposito, Valeria Fionda
备注: 15 pages, 2 figures

点击查看摘要

Abstract:This paper considers the possibility that abstract thinking and advanced synthesis skills might encourage extraterrestrial civilizations to accept communication with mankind on Earth. For this purpose, a notation not relying upon the use of alphabet and numbers is proposed, in order to denote just some basic geometric structures of current physical theories: vector fields, one-form fields, and tensor fields of arbitrary order. An advanced civilization might appreciate the way here proposed to achieve a concise description of electromagnetism and general relativity, and hence it might accept the challenge of responding to our signals. The abstract symbols introduced in this paper to describe the basic structures of physical theories are encoded into black and white bitmap images that can be easily converted into short bit sequences and modulated on a carrier wave for radio transmission.

NLP-81-标题: Inserting Faces inside Captions: Image Captioning with Attention Guided Merging

链接: https://arxiv.org/abs/2405.02305
作者: Yannis Tevissen (ARMEDIA-SAMOVAR, ML), Khalil Guetari, Marine Tassel, Erwan Kerleroux, Frédéric Petitpont
备注:

点击查看摘要

Abstract:Image captioning models are widely used to describe recent and archived pictures with the objective of improving their accessibility and retrieval. Yet, these approaches tend to be inefficient and biased at retrieving people’s names. In this work we introduce AstroCaptions, a dataset for the image captioning task. This dataset specifically contains thousands of public fig-ures that are complex to identify for a traditional model. We also propose a novel post-processing method to insert identified people’s names inside the caption using explainable AI tools and the grounding capabilities of vi-sion-language models. The results obtained with this method show signifi-cant improvements of captions quality and a potential of reducing halluci-nations. Up to 93.2% of the persons detected can be inserted in the image captions leading to improvements in the BLEU, ROUGE, CIDEr and METEOR scores of each captioning model.

机器学习

ML-0-标题: Why is SAM Robust to Label Noise?

链接: https://arxiv.org/abs/2405.03676
作者: Christina Baek, Zico Kolter, Aditi Raghunathan
备注:

点击查看摘要

Abstract:Sharpness-Aware Minimization (SAM) is most known for achieving state-of the-art performances on natural image and language tasks. However, its most pronounced improvements (of tens of percent) is rather in the presence of label noise. Understanding SAM’s label noise robustness requires a departure from characterizing the robustness of minimas lying in “flatter” regions of the loss landscape. In particular, the peak performance under label noise occurs with early stopping, far before the loss converges. We decompose SAM’s robustness into two effects: one induced by changes to the logit term and the other induced by changes to the network Jacobian. The first can be observed in linear logistic regression where SAM provably up-weights the gradient contribution from clean examples. Although this explicit up-weighting is also observable in neural networks, when we intervene and modify SAM to remove this effect, surprisingly, we see no visible degradation in performance. We infer that SAM’s effect in deeper networks is instead explained entirely by the effect SAM has on the network Jacobian. We theoretically derive the implicit regularization induced by this Jacobian effect in two layer linear networks. Motivated by our analysis, we see that cheaper alternatives to SAM that explicitly induce these regularization effects largely recover the benefits in deep networks trained on real-world datasets.

ML-1-标题: Cutting through buggy adversarial example defenses: fixing 1 line of code breaks Sabre

链接: https://arxiv.org/abs/2405.03672
作者: Nicholas Carlini
备注:

点击查看摘要

Abstract:Sabre is a defense to adversarial examples that was accepted at IEEE S&P 2024. We first reveal significant flaws in the evaluation that point to clear signs of gradient masking. We then show the cause of this gradient masking: a bug in the original evaluation code. By fixing a single line of code in the original repository, we reduce Sabre’s robust accuracy to 0%. In response to this, the authors modify the defense and introduce a new defense component not described in the original paper. But this fix contains a second bug; modifying one more line of code reduces robust accuracy to below baseline levels.

ML-2-标题: A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions

链接: https://arxiv.org/abs/2405.03664
作者: Sharath Raghvendra, Pouyan Shirzadian, Kaiyi Zhang
备注:

点击查看摘要

Abstract:The 2 -Wasserstein distance is sensitive to minor geometric differences between distributions, making it a very powerful dissimilarity metric. However, due to this sensitivity, a small outlier mass can also cause a significant increase in the 2 -Wasserstein distance between two similar distributions. Similarly, sampling discrepancy can cause the empirical 2 -Wasserstein distance on n samples in \mathbbR^2 to converge to the true distance at a rate of n^-1/4 , which is significantly slower than the rate of n^-1/2 for 1 -Wasserstein distance. We introduce a new family of distances parameterized by k \ge 0 , called k -RPW, that is based on computing the partial 2 -Wasserstein distance. We show that (1) k -RPW satisfies the metric properties, (2) k -RPW is robust to small outlier mass while retaining the sensitivity of 2 -Wasserstein distance to minor geometric differences, and (3) when k is a constant, k -RPW distance between empirical distributions on n samples in \mathbbR^2 converges to the true distance at a rate of n^-1/3 , which is faster than the convergence rate of n^-1/4 for the 2 -Wasserstein distance. Using the partial p -Wasserstein distance, we extend our distance to any p \in [1,\infty] . By setting parameters k or p appropriately, we can reduce our distance to the total variation, p -Wasserstein, and the Lévy-Prokhorov distances. Experiments show that our distance function achieves higher accuracy in comparison to the 1 -Wasserstein, 2 -Wasserstein, and TV distances for image retrieval tasks on noisy real-world data sets.

ML-3-标题: Competitive strategies to use “warm start” algorithms with predictions

链接: https://arxiv.org/abs/2405.03661
作者: Vaidehi Srinivas, Avrim Blum
备注:

点击查看摘要

Abstract:We consider the problem of learning and using predictions for warm start algorithms with predictions. In this setting, an algorithm is given an instance of a problem, and a prediction of the solution. The runtime of the algorithm is bounded by the distance from the predicted solution to the true solution of the instance. Previous work has shown that when instances are drawn iid from some distribution, it is possible to learn an approximately optimal fixed prediction (Dinitz et al, NeurIPS 2021), and in the adversarial online case, it is possible to compete with the best fixed prediction in hindsight (Khodak et al, NeurIPS 2022). In this work we give competitive guarantees against stronger benchmarks that consider a set of k predictions \mathbfP . That is, the “optimal offline cost” to solve an instance with respect to \mathbfP is the distance from the true solution to the closest member of \mathbfP . This is analogous to the k -medians objective function. In the distributional setting, we show a simple strategy that incurs cost that is at most an O(k) factor worse than the optimal offline cost. We then show a way to leverage learnable coarse information, in the form of partitions of the instance space into groups of “similar” instances, that allows us to potentially avoid this O(k) factor. Finally, we consider an online version of the problem, where we compete against offline strategies that are allowed to maintain a moving set of k predictions or “trajectories,” and are charged for how much the predictions move. We give an algorithm that does at most O(k^4 \ln^2 k) times as much work as any offline strategy of k trajectories. This algorithm is deterministic (robust to an adaptive adversary), and oblivious to the setting of k . Thus the guarantee holds for all k simultaneously.

ML-4-标题: A review on data-driven constitutive laws for solids

链接: https://arxiv.org/abs/2405.03658
作者: Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N. Vlassis, Moritz Flaschel, Pietro Carrara, Laura De Lorenzis
备注: 57 pages, 7 Figures

点击查看摘要

Abstract:This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized taxonomy to a large spectrum of methodologies developed in the past decades and to discuss the benefits and drawbacks of the various techniques for interpreting and forecasting mechanics behavior across different scales. Distinguishing between machine-learning-based and model-free methods, we further categorize approaches based on their interpretability and on their learning process/type of required data, while discussing the key problems of generalization and trustworthiness. We attempt to provide a road map of how these can be reconciled in a data-availability-aware context. We also touch upon relevant aspects such as data sampling techniques, design of experiments, verification, and validation.

ML-5-标题: Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders ICLR2024

链接: https://arxiv.org/abs/2405.03651
作者: Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum
备注: ICLR 2024

点击查看摘要

Abstract:Cross-encoder (CE) models which compute similarity by jointly encoding a query-item pair perform better than embedding-based models (dual-encoders) at estimating query-item relevance. Existing approaches perform k-NN search with CE by approximating the CE similarity with a vector embedding space fit either with dual-encoders (DE) or CUR matrix factorization. DE-based retrieve-and-rerank approaches suffer from poor recall on new domains and the retrieval with DE is decoupled from the CE. While CUR-based approaches can be more accurate than the DE-based approach, they require a prohibitively large number of CE calls to compute item embeddings, thus making it impractical for deployment at scale. In this paper, we address these shortcomings with our proposed sparse-matrix factorization based method that efficiently computes latent query and item embeddings to approximate CE scores and performs k-NN search with the approximate CE similarity. We compute item embeddings offline by factorizing a sparse matrix containing query-item CE scores for a set of train queries. Our method produces a high-quality approximation while requiring only a fraction of CE calls as compared to CUR-based methods, and allows for leveraging DE to initialize the embedding space while avoiding compute- and resource-intensive finetuning of DE via distillation. At test time, the item embeddings remain fixed and retrieval occurs over rounds, alternating between a) estimating the test query embedding by minimizing error in approximating CE scores of items retrieved thus far, and b) using the updated test query embedding for retrieving more items. Our k-NN search method improves recall by up to 5% (k=1) and 54% (k=100) over DE-based approaches. Additionally, our indexing approach achieves a speedup of up to 100x over CUR-based and 5x over DE distillation methods, while matching or improving k-NN search recall over baselines.

ML-6-标题: Collage: Light-Weight Low-Precision Strategy for LLM Training ICML2024

链接: https://arxiv.org/abs/2405.03637
作者: Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan
备注: ICML 2024

点击查看摘要

Abstract:Large models training is plagued by the intense compute cost and limited hardware memory. A practical solution is low-precision representation but is troubled by loss in numerical accuracy and unstable training rendering the model less useful. We argue that low-precision floating points can perform well provided the error is properly compensated at the critical locations in the training process. We propose Collage which utilizes multi-component float representation in low-precision to accurately perform operations with numerical errors accounted. To understand the impact of imprecision to training, we propose a simple and novel metric which tracks the lost information during training as well as differentiates various precision strategies. Our method works with commonly used low-precision such as half-precision ( 16 -bit floating points) and can be naturally extended to work with even lower precision such as 8 -bit. Experimental results show that pre-training using Collage removes the requirement of using 32 -bit floating-point copies of the model and attains similar/better training performance compared to (16, 32) -bit mixed-precision strategy, with up to 3.7\times speedup and \sim 15% to 23% less memory usage in practice.

ML-7-标题: Federated Learning Privacy: Attacks Defenses Applications and Policy Landscape - A Survey

链接: https://arxiv.org/abs/2405.03636
作者: Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin S. Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger R. Roth
备注: Submitted to ACM Computing Surveys

点击查看摘要

Abstract:Deep learning has shown incredible potential across a vast array of tasks and accompanying this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology enabling collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be “reverse engineered” to infer information about the private training data. It has been shown under a wide variety of settings that this premise for privacy does \em not hold. In this survey paper, we provide a comprehensive literature review of the different privacy attacks and defense methods in FL. We identify the current limitations of these attacks and highlight the settings in which FL client privacy can be broken. We dissect some of the successful industry applications of FL and draw lessons for future successful adoption. We survey the emerging landscape of privacy regulation for FL. We conclude with future directions for taking FL toward the cherished goal of generating accurate models while preserving the privacy of the data from its participants.

ML-8-标题: ε-Policy Gradient for Online Pricing

链接: https://arxiv.org/abs/2405.03624
作者: Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
备注:

点击查看摘要

Abstract:Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an \epsilon -policy gradient algorithm for the online pricing learning task. The algorithm extends \epsilon -greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying the exploration cost in terms of the exploration probability \epsilon and the exploitation cost in terms of the gradient descent optimization and gradient estimation errors. The algorithm achieves an expected regret of order \mathcalO(\sqrtT) (up to a logarithmic factor) over T trials.

ML-9-标题: Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey

链接: https://arxiv.org/abs/2405.03615
作者: Farid Saberi-Movahed, Kamal Berahman, Razieh Sheikhpour, Yuefeng Li, Shirui Pan
备注: 10 Paes, 2 figures, to be appear in acm computing survey

点击查看摘要

Abstract:Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data. Nonnegative Matrix Factorization (NMF) has emerged as a popular and powerful method for dimensionality reduction. Despite its extensive use, there remains a need for a comprehensive analysis of NMF in the context of dimensionality reduction. To address this gap, this paper presents a comprehensive survey of NMF, focusing on its applications in both feature extraction and feature selection. We introduce a classification of dimensionality reduction, enhancing understanding of the underlying concepts. Subsequently, we delve into a thorough summary of diverse NMF approaches used for feature extraction and selection. Furthermore, we discuss the latest research trends and potential future directions of NMF in dimensionality reduction, aiming to highlight areas that need further exploration and development.

ML-10-标题: Deep Clustering with Self-Supervision using Pairwise Similarities

链接: https://arxiv.org/abs/2405.03590
作者: Mohammadreza Sadeghi, Narges Armanfard
备注:

点击查看摘要

Abstract:Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propose a novel deep clustering framework with self-supervision using pairwise similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder’s latent space. In the second phase, we propose to employ pairwise similarities to create a K -dimensional space that is capable of accommodating more complex cluster distributions, hence providing more accurate clustering performance. K is the number of clusters. The autoencoder’s latent space obtained in the first phase is used as the input of the second phase. The effectiveness of both phases is demonstrated on seven benchmark datasets by conducting a rigorous set of experiments.

ML-11-标题: Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting

链接: https://arxiv.org/abs/2405.03582
作者: Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme
备注:

点击查看摘要

Abstract:Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challenge to standard deep learn- ing models that operate only on fully observed and regularly sampled time series. In order to capture the continuous dynamics of the irreg- ular time series, many models rely on solving an Ordinary Differential Equation (ODE) in the hidden state. These ODE-based models tend to perform slow and require large memory due to sequential operations and a complex ODE solver. As an alternative to complex ODE-based mod- els, we propose a family of models called Functional Latent Dynamics (FLD). Instead of solving the ODE, we use simple curves which exist at all time points to specify the continuous latent state in the model. The coefficients of these curves are learned only from the observed values in the time series ignoring the missing values. Through extensive experi- ments, we demonstrate that FLD achieves better performance compared to the best ODE-based model while reducing the runtime and memory overhead. Specifically, FLD requires an order of magnitude less time to infer the forecasts compared to the best performing forecasting model.

ML-12-标题: ILILT: Implicit Learning of Inverse Lithography Technologies ICML24

链接: https://arxiv.org/abs/2405.03574
作者: Haoyu Yang, Haoxing Ren
备注: 12 pages, 10 figures, accepted by International Conference on Machine Learning (ICML24)

点击查看摘要

Abstract:Lithography, transferring chip design masks to the silicon wafer, is the most important phase in modern semiconductor manufacturing flow. Due to the limitations of lithography systems, Extensive design optimizations are required to tackle the design and silicon mismatch. Inverse lithography technology (ILT) is one of the promising solutions to perform pre-fabrication optimization, termed mask optimization. Because of mask optimization problems’ constrained non-convexity, numerical ILT solvers rely heavily on good initialization to avoid getting stuck on sub-optimal solutions. Machine learning (ML) techniques are hence proposed to generate mask initialization for ILT solvers with one-shot inference, targeting faster and better convergence during ILT. This paper addresses the question of \textitwhether ML models can directly generate high-quality optimized masks without engaging ILT solvers in the loop. We propose an implicit learning ILT framework: ILILT, which leverages the implicit layer learning method and lithography-conditioned inputs to ground the model. Trained to understand the ILT optimization procedure, ILILT can outperform the state-of-the-art machine learning solutions, significantly improving efficiency and quality.

ML-13-标题: Position Paper: Leveraging Foundational Models for Black-Box Optimization: Benefits Challenges and Future Directions ICML

链接: https://arxiv.org/abs/2405.03547
作者: Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, Yutian Chen
备注: Accepted to International Conference on Machine Learning (ICML) 2024

点击查看摘要

Abstract:Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fields such as reinforcement learning, robotics, and computer vision. Their incorporation has been rapid and transformative, marking a significant paradigm shift in the field of machine learning research. However, the field of experimental design, grounded on black-box optimization, has been much less affected by such a paradigm shift, even though integrating LLMs with optimization presents a unique landscape ripe for exploration. In this position paper, we frame the field of black-box optimization around sequence-based foundation models and organize their relationship with previous literature. We discuss the most promising ways foundational language models can revolutionize optimization, which include harnessing the vast wealth of information encapsulated in free-form text to enrich task comprehension, utilizing highly flexible sequence models such as Transformers to engineer superior optimization strategies, and enhancing performance prediction over previously unseen search spaces.

ML-14-标题: Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation

链接: https://arxiv.org/abs/2405.03537
作者: Jesher Joshua M, Adhithya R, Sree Dananjay S, M Revathi
备注:

点击查看摘要

Abstract:Web phishing poses a dynamic threat, requiring detection systems to quickly adapt to the latest tactics. Traditional approaches of accumulating data and periodically retraining models are outpaced. We propose a novel paradigm combining federated learning and continual learning, enabling distributed nodes to continually update models on streams of new phishing data, without accumulating data. These locally adapted models are then aggregated at a central server via federated learning. To enhance detection, we introduce a custom attention-based classifier model with residual connections, tailored for web phishing, leveraging attention mechanisms to capture intricate phishing patterns. We evaluate our hybrid learning paradigm across continual learning strategies (cumulative, replay, MIR, LwF) and model architectures through an empirical investigation. Our main contributions are: (1) a new hybrid federated-continual learning paradigm for robust web phishing detection, and (2) a novel attention + residual connections based model explicitly designed for this task, attaining 0.93 accuracy, 0.90 precision, 0.96 recall and 0.93 f1-score with the LwF strategy, outperforming traditional approaches in detecting emerging phishing threats while retaining past knowledge.

ML-15-标题: Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer ICLR2024

链接: https://arxiv.org/abs/2405.03534
作者: Xingyu Liu, Deepak Pathak, Ding Zhao
备注: ICLR 2024

点击查看摘要

Abstract:We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named Meta - Evolve that uses continuous robot evolution to efficiently transfer the policy to each target robot through a set of tree-structured evolutionary robot sequences. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer. We present a heuristic approach to determine an optimized robot evolution tree. Experiments have shown that our method is able to improve the efficiency of one-to-three transfer of manipulation policy by up to 3.2 \times and one-to-six transfer of agile locomotion policy by 2.4 \times in terms of simulation cost over the baseline of launching multiple independent one-to-one policy transfers.

ML-16-标题: ReinWiFi: A Reinforcement-Learning-Based Framework for the Application-Layer QoS Optimization of WiFi Networks

链接: https://arxiv.org/abs/2405.03526
作者: Qianren Li, Bojie Lv, Yuncong Hong, Rui Wang
备注:

点击查看摘要

Abstract:In this paper, a reinforcement-learning-based scheduling framework is proposed and implemented to optimize the application-layer quality-of-service (QoS) of a practical wireless local area network (WLAN) suffering from unknown interference. Particularly, application-layer tasks of file delivery and delay-sensitive communication, e.g., screen projection, in a WLAN with enhanced distributed channel access (EDCA) mechanism, are jointly scheduled by adjusting the contention window sizes and application-layer throughput limitation, such that their QoS, including the throughput of file delivery and the round trip time of the delay-sensitive communication, can be optimized. Due to the unknown interference and vendor-dependent implementation of the network interface card, the relation between the scheduling policy and the system QoS is unknown. Hence, a reinforcement learning method is proposed, in which a novel Q-network is trained to map from the historical scheduling parameters and QoS observations to the current scheduling action. It is demonstrated on a testbed that the proposed framework can achieve a significantly better QoS than the conventional EDCA mechanism.

ML-17-标题: GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge

链接: https://arxiv.org/abs/2405.03516
作者: Jin Qian, Kaimin Wei, Yongdong Wu, Jilian Zhang, Jipeng Chen, Huan Bao
备注: 18 pages, 10 figures, conference

点击查看摘要

Abstract:Federated learning (FL) has emerged as a privacy-preserving machine learning approach where multiple parties share gradient information rather than original user data. Recent work has demonstrated that gradient inversion attacks can exploit the gradients of FL to recreate the original user data, posing significant privacy risks. However, these attacks make strong assumptions about the attacker, such as altering the model structure or parameters, gaining batch normalization statistics, or acquiring prior knowledge of the original training set, etc. Consequently, these attacks are not possible in real-world scenarios. To end it, we propose a novel Gradient Inversion attack based on Style Migration Network (GI-SMN), which breaks through the strong assumptions made by previous gradient inversion attacks. The optimization space is reduced by the refinement of the latent code and the use of regular terms to facilitate gradient matching. GI-SMN enables the reconstruction of user data with high similarity in batches. Experimental results have demonstrated that GI-SMN outperforms state-of-the-art gradient inversion attacks in both visual effect and similarity metrics. Additionally, it also can overcome gradient pruning and differential privacy defenses.

ML-18-标题: Whispy: Adapting STT Whisper Models to Real-Time Environments

链接: https://arxiv.org/abs/2405.03484
作者: Antonio Bevilacqua, Paolo Saviano, Alessandro Amirante, Simon Pietro Romano
备注:

点击查看摘要

Abstract:Large general-purpose transformer models have recently become the mainstay in the realm of speech analysis. In particular, Whisper achieves state-of-the-art results in relevant tasks such as speech recognition, translation, language identification, and voice activity detection. However, Whisper models are not designed to be used in real-time conditions, and this limitation makes them unsuitable for a vast plethora of practical applications. In this paper, we introduce Whispy, a system intended to bring live capabilities to the Whisper pretrained models. As a result of a number of architectural optimisations, Whispy is able to consume live audio streams and generate high level, coherent voice transcriptions, while still maintaining a low computational cost. We evaluate the performance of our system on a large repository of publicly available speech datasets, investigating how the transcription mechanism introduced by Whispy impacts on the Whisper output. Experimental results show how Whispy excels in robustness, promptness, and accuracy.

ML-19-标题: AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformer s

链接: https://arxiv.org/abs/2405.03481
作者: Wenhao Zhu, Guojie Song, Liang Wang, Shaoguo Liu
备注:

点击查看摘要

Abstract:Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and demonstrating promising performance and expressive power. However, the quadratic complexity of self-attention mechanism in GTs has limited their scalability, and previous approaches to address this issue often suffer from expressiveness degradation or lack of versatility. To address this issue, we propose AnchorGT, a novel attention architecture for GTs with global receptive field and almost linear complexity, which serves as a flexible building block to improve the scalability of a wide range of GT models. Inspired by anchor-based GNNs, we employ structurally important k -dominating node set as anchors and design an attention mechanism that focuses on the relationship between individual nodes and anchors, while retaining the global receptive field for all nodes. With its intuitive design, AnchorGT can easily replace the attention module in various GT models with different network architectures and structural encodings, resulting in reduced computational overhead without sacrificing performance. In addition, we theoretically prove that AnchorGT attention can be strictly more expressive than Weisfeiler-Lehman test, showing its superiority in representing graph structures. Our experiments on three state-of-the-art GT models demonstrate that their AnchorGT variants can achieve better results while being faster and significantly more memory efficient.

ML-20-标题: Byzantine-Robust Gossip: Insights from a Dual Approach

链接: https://arxiv.org/abs/2405.03449
作者: Renaud Gaucher, Hadrien Hendrikx, Aymeric Dieuleveut
备注: 9 pages, 1 figure

点击查看摘要

Abstract:Distributed approaches have many computational benefits, but they are vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly with one another. We leverage the so-called dual approach to design a general robust decentralized optimization method. We provide both global and local clipping rules in the special case of average consensus, with tight convergence guarantees. These clipping rules are practical, and yield results that finely characterize the impact of Byzantine nodes, highlighting for instance a qualitative difference in convergence between global and local clipping thresholds. Lastly, we demonstrate that they can serve as a basis for designing efficient attacks.

ML-21-标题: Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery ICRA2024

链接: https://arxiv.org/abs/2405.03440
作者: Kento Kawaharazuka, Kei Okada, Masayuki Inaba
备注: Accepted at ICRA2024, website - this https URL

点击查看摘要

Abstract:In this study, we present an implementation strategy for a robot that performs peg transfer tasks in Fundamentals of Laparoscopic Surgery (FLS) via imitation learning, aimed at the development of an autonomous robot for laparoscopic surgery. Robotic laparoscopic surgery presents two main challenges: (1) the need to manipulate forceps using ports established on the body surface as fulcrums, and (2) difficulty in perceiving depth information when working with a monocular camera that displays its images on a monitor. Especially, regarding issue (2), most prior research has assumed the availability of depth images or models of a target to be operated on. Therefore, in this study, we achieve more accurate imitation learning with only monocular images by extracting motion constraints from one exemplary motion of skilled operators, collecting data based on these constraints, and conducting imitation learning based on the collected data. We implemented an overall system using two Franka Emika Panda Robot Arms and validated its effectiveness.

ML-22-标题: Improved Forward-Forward Contrastive Learning

链接: https://arxiv.org/abs/2405.03432
作者: Gananath R
备注:

点击查看摘要

Abstract:The backpropagation algorithm, or backprop, is a widely utilized optimization technique in deep learning. While there’s growing evidence suggesting that models trained with backprop can accurately explain neuronal data, no backprop-like method has yet been discovered in the biological brain for learning. Moreover, employing a naive implementation of backprop in the brain has several drawbacks. In 2022, Geoffrey Hinton proposed a biologically plausible learning method known as the Forward-Forward (FF) algorithm. Shortly after this paper, a modified version called FFCL was introduced. However, FFCL had limitations, notably being a three-stage learning system where the final stage still relied on regular backpropagation. In our approach, we address these drawbacks by eliminating the last two stages of FFCL and completely removing regular backpropagation. Instead, we rely solely on local updates, offering a more biologically plausible alternative.

ML-23-标题: ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformer s

链接: https://arxiv.org/abs/2405.03429
作者: Arvid Weyrauch, Thomas Steens, Oskar Taubert, Benedikt Hanke, Aslan Eqbal, Ewa Götz, Achim Streit, Markus Götz, Charlotte Debus
备注: 9 pages, 3 figures, to be published at IEEE CAI 2024, Associated code available at this https URL

点击查看摘要

Abstract:Transformers have recently gained prominence in long time series forecasting by elevating accuracies in a variety of use cases. Regrettably, in the race for better predictive performance the overhead of model architectures has grown onerous, leading to models with computational demand infeasible for most practical applications. To bridge the gap between high method complexity and realistic computational resources, we introduce the Residual Cyclic Transformer, ReCycle. ReCycle utilizes primary cycle compression to address the computational complexity of the attention mechanism in long time series. By learning residuals from refined smoothing average techniques, ReCycle surpasses state-of-the-art accuracy in a variety of application use cases. The reliable and explainable fallback behavior ensured by simple, yet robust, smoothing average techniques additionally lowers the barrier for user acceptance. At the same time, our approach reduces the run time and energy consumption by more than an order of magnitude, making both training and inference feasible on low-performance, low-power and edge computing devices. Code is available at this https URL

ML-24-标题: Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials

链接: https://arxiv.org/abs/2405.03427
作者: Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier, Christophe Millet, Mathilde Mougeot
备注: 28 pages, 26 figures, 4 tables

点击查看摘要

Abstract:Physics-Informed Neural Networks (PINNs) have gained considerable interest in diverse engineering domains thanks to their capacity to integrate physical laws into deep learning models. Recently, geometry-aware PINN-based approaches that employ the strong form of underlying physical system equations have been developed with the aim of integrating geometric information into PINNs. Despite ongoing research, the assessment of PINNs in problems with various geometries remains an active area of investigation. In this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.

ML-25-标题: Automated Metaheuristic Algorithm Design with Autoregressive Learning

链接: https://arxiv.org/abs/2405.03419
作者: Qi Zhao, Tengfei Liu, Bai Yan, Qiqi Duan, Jian Yang, Yuhui Shi
备注:

点击查看摘要

Abstract:Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap towards fully discovering potentials over the metaheuristic family and fertilizing from prior design experience. To bridge the gap, this paper proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms. Our designer formulates metaheuristic algorithm design as a sequence generation task, and harnesses an autoregressive generative network to handle the task. This offers two advances. First, through autoregressive inference, the designer generates algorithms with diverse lengths and structures, enabling to fully discover potentials over the metaheuristic family. Second, prior design knowledge learned and accumulated in neurons of the designer can be retrieved for designing algorithms for future problems, paving the way to continual design of algorithms for open-ended problem-solving. Extensive experiments on numeral benchmarks and real-world problems reveal that the proposed designer generates algorithms that outperform all human-created baselines on 24 out of 25 test problems. The generated algorithms display various structures and behaviors, reasonably fitting for different problem-solving contexts. Code will be released after paper publication.

ML-26-标题: LightTR: A Lightweight Framework for Federated Trajectory Recovery ICDE2024

链接: https://arxiv.org/abs/2405.03409
作者: Ziqiao Liu, Hao Miao, Yan Zhao, Chenxi Liu, Kai Zheng, Huan Li
备注: The paper was accepted by ICDE 2024

点击查看摘要

Abstract:With the proliferation of GPS-equipped edge devices, huge trajectory data is generated and accumulated in various domains, motivating a variety of urban applications. Due to the limited acquisition capabilities of edge devices, a lot of trajectories are recorded at a low sampling rate, which may lead to the effectiveness drop of urban applications. We aim to recover a high-sampled trajectory based on the low-sampled trajectory in free space, i.e., without road network information, to enhance the usability of trajectory data and support urban applications more effectively. Recent proposals targeting trajectory recovery often assume that trajectories are available at a central location, which fail to handle the decentralized trajectories and hurt privacy. To bridge the gap between decentralized training and trajectory recovery, we propose a lightweight framework, LightTR, for federated trajectory recovery based on a client-server architecture, while keeping the data decentralized and private in each client/platform center (e.g., each data center of a company). Specifically, considering the limited processing capabilities of edge devices, LightTR encompasses a light local trajectory embedding module that offers improved computational efficiency without compromising its feature extraction capabilities. LightTR also features a meta-knowledge enhanced local-global training scheme to reduce communication costs between the server and clients and thus further offer efficiency improvement. Extensive experiments demonstrate the effectiveness and efficiency of the proposed framework.

ML-27-标题: E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification

链接: https://arxiv.org/abs/2405.03401
作者: Xin Zhang, Daochen Zha, Qiaoyu Tan
备注:

点击查看摘要

Abstract:This work studies ensemble learning for graph neural networks (GNNs) under the popular semi-supervised setting. Ensemble learning has shown superiority in improving the accuracy and robustness of traditional machine learning by combining the outputs of multiple weak learners. However, adopting a similar idea to integrate different GNN models is challenging because of two reasons. First, GNN is notorious for its poor inference ability, so naively assembling multiple GNN models would deteriorate the inference efficiency. Second, when GNN models are trained with few labeled nodes, their performance are limited. In this case, the vanilla ensemble approach, e.g., majority vote, may be sub-optimal since most base models, i.e., GNNs, may make the wrong predictions. To this end, in this paper, we propose an efficient ensemble learner–E2GNN to assemble multiple GNNs in a learnable way by leveraging both labeled and unlabeled nodes. Specifically, we first pre-train different GNN models on a given data scenario according to the labeled nodes. Next, instead of directly combing their outputs for label inference, we train a simple multi-layer perceptron–MLP model to mimic their predictions on both labeled and unlabeled nodes. Then the unified MLP model is deployed to infer labels for unlabeled or new nodes. Since the predictions of unlabeled nodes from different GNN models may be incorrect, we develop a reinforced discriminator to effectively filter out those wrongly predicted nodes to boost the performance of MLP. By doing this, we suggest a principled approach to tackle the inference issues of GNN ensembles and maintain the merit of ensemble learning: improved performance. Comprehensive experiments over both transductive and inductive settings, across different GNN backbones and 8 benchmark datasets, demonstrate the superiority of E2GNN.

ML-28-标题: Dont Waste Your Time: Early Stopping Cross-Validation

链接: https://arxiv.org/abs/2405.03389
作者: Edward Bergman, Lennart Purucker, Frank Hutter
备注: Accepted at Third International Conference on Automated Machine Learning (AutoML 2024); for code, see this https URL

点击查看摘要

Abstract:State-of-the-art automated machine learning systems for tabular data often employ cross-validation; ensuring that measured performances generalize to unseen data, or that subsequent ensembling does not overfit. However, using k-fold cross-validation instead of holdout validation drastically increases the computational cost of validating a single configuration. While ensuring better generalization and, by extension, better performance, the additional cost is often prohibitive for effective model selection within a time budget. We aim to make model selection with cross-validation more effective. Therefore, we study early stopping the process of cross-validation during model selection. We investigate the impact of early stopping on random search for two algorithms, MLP and random forest, across 36 classification datasets. We further analyze the impact of the number of folds by considering 3-, 5-, and 10-folds. In addition, we investigate the impact of early stopping with Bayesian optimization instead of random search and also repeated cross-validation. Our exploratory study shows that even a simple-to-understand and easy-to-implement method consistently allows model selection to converge faster; in ~94% of all datasets, on average by ~214%. Moreover, stopping cross-validation enables model selection to explore the search space more exhaustively by considering +167% configurations on average within one hour, while also obtaining better overall performance.

ML-29-标题: Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension

链接: https://arxiv.org/abs/2405.03386
作者: Marek Herde, Lukas Lührs, Denis Huseljic, Bernhard Sick
备注: Under review

点击查看摘要

Abstract:Training with noisy class labels impairs neural networks’ generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at this https URL.

ML-30-标题: GLIP: Electromagnetic Field Exposure Map Completion by Deep Generative Networks

链接: https://arxiv.org/abs/2405.03384
作者: Mohammed Mallik, Davy P. Gaillot, Laurent Clavier
备注:

点击查看摘要

Abstract:In Spectrum cartography (SC), the generation of exposure maps for radio frequency electromagnetic fields (RF-EMF) spans dimensions of frequency, space, and time, which relies on a sparse collection of sensor data, posing a challenging ill-posed inverse problem. Cartography methods based on models integrate designed priors, such as sparsity and low-rank structures, to refine the solution of this inverse problem. In our previous work, EMF exposure map reconstruction was achieved by Generative Adversarial Networks (GANs) where physical laws or structural constraints were employed as a prior, but they require a large amount of labeled data or simulated full maps for training to produce efficient results. In this paper, we present a method to reconstruct EMF exposure maps using only the generator network in GANs which does not require explicit training, thus overcoming the limitations of GANs, such as using reference full exposure maps. This approach uses a prior from sensor data as Local Image Prior (LIP) captured by deep convolutional generative networks independent of learning the network parameters from images in an urban environment. Experimental results show that, even when only sparse sensor data are available, our method can produce accurate estimates.

ML-31-标题: Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning ICLR2024

链接: https://arxiv.org/abs/2405.03379
作者: Stone Tao, Arth Shukla, Tse-kai Chan, Hao Su
备注: Accepted at The Twelfth International Conference on Learning Representations (ICLR 2024). Website: this https URL

点击查看摘要

Abstract:Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve complex tasks from sparse rewards. One direction includes augmenting RL with offline data demonstrating desired tasks, but past work often require a lot of high-quality demonstration data that is difficult to obtain, especially for domains such as robotics. Our approach consists of a reverse curriculum followed by a forward curriculum. Unique to our approach compared to past work is the ability to efficiently leverage more than one demonstration via a per-demonstration reverse curriculum generated via state resets. The result of our reverse curriculum is an initial policy that performs well on a narrow initial state distribution and helps overcome difficult exploration problems. A forward curriculum is then used to accelerate the training of the initial policy to perform well on the full initial state distribution of the task and improve demonstration and sample efficiency. We show how the combination of a reverse curriculum and forward curriculum in our method, RFCL, enables significant improvements in demonstration and sample efficiency compared against various state-of-the-art learning-from-demonstration baselines, even solving previously unsolvable tasks that require high precision and control.

ML-32-标题: Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation

链接: https://arxiv.org/abs/2405.03363
作者: Takekazu Kitagishi, Yuichi Hiroi, Yuna Watanabe, Yuta Itoh, Jun Rekimoto
备注: 10 pages, 8 figures, Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

点击查看摘要

Abstract:The tactile sensation of textiles is critical in determining the comfort of clothing. For remote use, such as online shopping, users cannot physically touch the textile of clothes, making it difficult to evaluate its tactile sensation. Tactile sensing and actuation devices are required to transmit the tactile sensation of textiles. The sensing device needs to recognize different garments, even with hand-held sensors. In addition, the existing actuation device can only present a limited number of known patterns and cannot transmit unknown tactile sensations of textiles. To address these issues, we propose Telextiles, an interface that can remotely transmit tactile sensations of textiles by creating a latent space that reflects the proximity of textiles through contrastive self-supervised learning. We confirm that textiles with similar tactile features are located close to each other in the latent space through a two-dimensional plot. We then compress the latent features for known textile samples into the 1D distance and apply the 16 textile samples to the rollers in the order of the distance. The roller is rotated to select the textile with the closest feature if an unknown textile is detected.

ML-33-标题: Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning ICML2024

链接: https://arxiv.org/abs/2405.03342
作者: Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao
备注: Accepted by ICML 2024

点击查看摘要

Abstract:Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e.g., leveraging neural networks to fit only one single nuisance function, may still encounter misspecification problems under networked interference without appropriate assumptions on the data generation process. To mitigate bias stemming from misspecification, we propose a novel doubly robust causal effect estimator under networked interference, by adapting the targeted learning technique to the training of neural networks. Specifically, we generalize the targeted learning technique into the networked interference setting and establish the condition under which an estimator achieves double robustness. Based on the condition, we devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss. Moreover, we provide a theoretical analysis of our designed estimator, revealing a faster convergence rate compared to a single nuisance model. Extensive experimental results on two real-world networks with semisynthetic data demonstrate the effectiveness of our proposed estimators.

ML-34-标题: Enhancing Q-Learning with Large Language Model Heuristics

链接: https://arxiv.org/abs/2405.03341
作者: Xiefeng Wu
备注: arXiv admin note: text overlap with arXiv:2009.06799 by other authors

点击查看摘要

Abstract:Q-learning excels in learning from feedback within sequential decision-making tasks but requires extensive sampling for significant improvements. Although reward shaping is a powerful technique for enhancing learning efficiency, it can introduce biases that affect agent performance. Furthermore, potential-based reward shaping is constrained as it does not allow for reward modifications based on actions or terminal states, potentially limiting its effectiveness in complex environments. Additionally, large language models (LLMs) can achieve zero-shot learning, but this is generally limited to simpler tasks. They also exhibit low inference speeds and occasionally produce hallucinations. To address these issues, we propose \textbfLLM-guided Q-learning that employs LLMs as heuristic to aid in learning the Q-function for reinforcement learning. It combines the advantages of both technologies without introducing performance bias. Our theoretical analysis demonstrates that the LLM heuristic provides action-level guidance. Additionally, our architecture has the capability to convert the impact of hallucinations into exploration costs. Moreover, the converged Q function corresponds to the MDP optimal Q function. Experiment results demonstrated that our algorithm enables agents to avoid ineffective exploration, enhances sampling efficiency, and is well-suited for complex control tasks.

ML-35-标题: Policy Learning for Balancing Short-Term and Long-Term Rewards

链接: https://arxiv.org/abs/2405.03329
作者: Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng
备注:

点击查看摘要

Abstract:Empirical researchers and decision-makers spanning various domains frequently seek profound insights into the long-term impacts of interventions. While the significance of long-term outcomes is undeniable, an overemphasis on them may inadvertently overshadow short-term gains. Motivated by this, this paper formalizes a new framework for learning the optimal policy that effectively balances both long-term and short-term rewards, where some long-term outcomes are allowed to be missing. In particular, we first present the identifiability of both rewards under mild assumptions. Next, we deduce the semiparametric efficiency bounds, along with the consistency and asymptotic normality of their estimators. We also reveal that short-term outcomes, if associated, contribute to improving the estimator of the long-term reward. Based on the proposed estimators, we develop a principled policy learning approach and further derive the convergence rates of regret and estimation errors associated with the learned policy. Extensive experiments are conducted to validate the effectiveness of the proposed method, demonstrating its practical applicability.

ML-36-标题: Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes

链接: https://arxiv.org/abs/2405.03327
作者: Xiaochen Zheng, Manuel Schürch, Xingyu Chen, Maria Angeliki Komninou, Reto Schüpbach, Ahmed Allam, Jan Bartussek, Michael Krauthammer
备注:

点击查看摘要

Abstract:The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics. Postoperative delirium (POD) is a complex neuropsychiatric condition with significant heterogeneity in its clinical manifestations and underlying pathophysiology. We hypothesize that POD comprises several distinct phenotypes, which cannot be directly observed in clinical practice. Identifying these phenotypes could enhance our understanding of POD pathogenesis and facilitate the development of targeted prevention and treatment strategies. In this paper, we propose an approach that combines supervised machine learning for personalized POD risk prediction with unsupervised clustering techniques to uncover potential POD phenotypes. We first demonstrate our approach using synthetic data, where we simulate patient cohorts with predefined phenotypes based on distinct sets of informative features. We aim to mimic any clinical disease with our synthetic data generation method. By training a predictive model and applying SHAP, we show that clustering patients in the SHAP feature importance space successfully recovers the true underlying phenotypes, outperforming clustering in the raw feature space. We then present a case study using real-world data from a cohort of elderly surgical patients. The results showcase the utility of our approach in uncovering clinically relevant subtypes of complex disorders like POD, paving the way for more precise and personalized treatment strategies.

ML-37-标题: Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction

链接: https://arxiv.org/abs/2405.03320
作者: Giuseppe Costantino, Sophie Giffard-Roisin, Mauro Dalla Mura, Anne Socquet
备注:

点击查看摘要

Abstract:Geospatial data has been transformative for the monitoring of the Earth, yet, as in the case of (geo)physical monitoring, the measurements can have variable spatial and temporal sampling and may be associated with a significant level of perturbations degrading the signal quality. Denoising geospatial data is, therefore, essential, yet often challenging because the observations may comprise noise coming from different origins, including both environmental signals and instrumental artifacts, which are spatially and temporally correlated, thus hard to disentangle. This study addresses the denoising of multivariate time series acquired by irregularly distributed networks of sensors, requiring specific methods to handle the spatiotemporal correlation of the noise and the signal of interest. Specifically, our method focuses on the denoising of geodetic position time series, used to monitor ground displacement worldwide with centimeter- to-millimeter precision. Among the signals affecting GNSS data, slow slip events (SSEs) are of interest to seismologists. These are transients of deformation that are weakly emerging compared to other signals. Here, we design SSEdenoiser, a multi-station spatiotemporal graph-based attentive denoiser that learns latent characteristics of GNSS noise to reveal SSE-related displacement with sub-millimeter precision. It is based on the key combination of graph recurrent networks and spatiotemporal Transformers. The proposed method is applied to the Cascadia subduction zone, where SSEs occur along with bursts of tectonic tremors, a seismic rumbling identified from independent seismic recordings. The extracted events match the spatiotemporal evolution of tremors. This good space-time correlation of the denoised GNSS signals with the tremors validates the proposed denoising procedure.

ML-38-标题: Provably Unlearnable Examples

链接: https://arxiv.org/abs/2405.03316
作者: Derui Wang, Minhui Xue, Bo Li, Seyit Camtepe, Liming Zhu
备注:

点击查看摘要

Abstract:The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. As a strategy to safeguard both data privacy and IP-related domain knowledge, efforts have been undertaken to render shared data unlearnable for unauthorized models in the wild. Existing methods apply empirically optimized perturbations to the data in the hope of disrupting the correlation between the inputs and the corresponding labels such that the data samples are converted into Unlearnable Examples (UEs). Nevertheless, the absence of mechanisms that can verify how robust the UEs are against unknown unauthorized models and train-time techniques engenders several problems. First, the empirically optimized perturbations may suffer from the problem of cross-model generalization, which echoes the fact that the unauthorized models are usually unknown to the defender. Second, UEs can be mitigated by train-time techniques such as data augmentation and adversarial training. Furthermore, we find that a simple recovery attack can restore the clean-task performance of the classifiers trained on UEs by slightly perturbing the learned weights. To mitigate the aforementioned problems, in this paper, we propose a mechanism for certifying the so-called (q, \eta) -Learnability of an unlearnable dataset via parametric smoothing. A lower certified (q, \eta) -Learnability indicates a more robust protection over the dataset. Finally, we try to 1) improve the tightness of certified (q, \eta) -Learnability and 2) design Provably Unlearnable Examples (PUEs) which have reduced (q, \eta) -Learnability. According to experimental results, PUEs demonstrate both decreased certified (q, \eta) -Learnability and enhanced empirical robustness compared to existing UEs.

ML-39-标题: Online Clustering of Known and Emerging Malware Families

链接: https://arxiv.org/abs/2405.03298
作者: Olha Jurečková, Martin Jureček, Mark Stamp
备注: arXiv admin note: text overlap with arXiv:2305.00605

点击查看摘要

Abstract:Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information security. Due to the large amount of malware samples available, it is essential to categorize malware samples according to their malicious characteristics. Clustering algorithms are thus becoming more widely used in computer security to analyze the behavior of malware variants and discover new malware families. Online clustering algorithms help us to understand malware behavior and produce a quicker response to new threats. This paper introduces a novel machine learning-based model for the online clustering of malicious samples into malware families. Streaming data is divided according to the clustering decision rule into samples from known and new emerging malware families. The streaming data is classified using the weighted k-nearest neighbor classifier into known families, and the online k-means algorithm clusters the remaining streaming data and achieves a purity of clusters from 90.20% for four clusters to 93.34% for ten clusters. This work is based on static analysis of portable executable files for the Windows operating system. Experimental results indicate that the proposed online clustering model can create high-purity clusters corresponding to malware families. This allows malware analysts to receive similar malware samples, speeding up their analysis.

ML-40-标题: Coefficient Decomposition for Spectral Graph Convolution

链接: https://arxiv.org/abs/2405.03296
作者: Feng Huang, Wen Zhang
备注:

点击查看摘要

Abstract:Spectral graph convolutional network (SGCN) is a kind of graph neural networks (GNN) based on graph signal filters, and has shown compelling expressivity for modeling graph-structured data. Most SGCNs adopt polynomial filters and learn the coefficients from the training data. Many of them focus on which polynomial basis leads to optimal expressive power and models’ architecture is little discussed. In this paper, we propose a general form in terms of spectral graph convolution, where the coefficients of polynomial basis are stored in a third-order tensor. Then, we show that the convolution block in existing SGCNs can be derived by performing a certain coefficient decomposition operation on the coefficient tensor. Based on the generalized view, we develop novel spectral graph convolutions CoDeSGC-CP and -Tucker by tensor decomposition CP and Tucker on the coefficient tensor. Extensive experimental results demonstrate that the proposed convolutions achieve favorable performance improvements.

ML-41-标题: End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability

链接: https://arxiv.org/abs/2405.03262
作者: Hinrikus Wolf, Luis Böttcher, Sarra Bouchkati, Philipp Lutat, Jens Breitung, Bastian Jung, Tina Möllemann, Viktor Todosijević, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Martin Grohe
备注:

点击查看摘要

Abstract:In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, will influence the power flow, especially in the distribution grids. Scalable methods that can make decisions for each grid connection are needed to enable congestion-free grid operation in the distribution grids. This paper presents a novel end-to-end approach to resolving congestion in distribution grids with deep reinforcement learning. Our architecture learns to curtail power and set appropriate reactive power to determine a non-congested and, thus, feasible grid state. State-of-the-art methods such as the optimal power flow (OPF) demand high computational costs and detailed measurements of every bus in a grid. In contrast, the presented method enables decisions under sparse information with just some buses observable in the grid. Distribution grids are generally not yet fully digitized and observable, so this method can be used for decision-making on the majority of low-voltage grids. On a real low-voltage grid the approach resolves 100% of violations in the voltage band and 98.8% of asset overloads. The results show that decisions can also be made on real grids that guarantee sufficient quality for congestion-free grid operation.

ML-42-标题: Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning IJCAI2024

链接: https://arxiv.org/abs/2405.03255
作者: Jiewen Deng, Renhe Jiang, Jiaqi Zhang, Xuan Song
备注: Accepted by IJCAI 2024 Main Track

点击查看摘要

Abstract:Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments. Despite significant strides in ST modeling in recent years, there remains a need to emphasize harnessing the potential of information from different modalities. Robust MoST forecasting is more challenging because it possesses (i) high-dimensional and complex internal structures and (ii) dynamic heterogeneity caused by temporal, spatial, and modality variations. In this study, we propose a novel MoST learning framework via Self-Supervised Learning, namely MoSSL, which aims to uncover latent patterns from temporal, spatial, and modality perspectives while quantifying dynamic heterogeneity. Experiment results on two real-world MoST datasets verify the superiority of our approach compared with the state-of-the-art baselines. Model implementation is available at this https URL.

ML-43-标题: Exploring the Frontiers of Softmax: Provable Optimization Applications in Diffusion Model and Beyond

链接: https://arxiv.org/abs/2405.03251
作者: Jiuxiang Gu, Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song
备注: 53 pages

点击查看摘要

Abstract:The softmax activation function plays a crucial role in the success of large language models (LLMs), particularly in the self-attention mechanism of the widely adopted Transformer architecture. However, the underlying learning dynamics that contribute to the effectiveness of softmax remain largely unexplored. As a step towards better understanding, this paper provides a theoretical study of the optimization and generalization properties of two-layer softmax neural networks, providing theoretical insights into their superior performance as other activation functions, such as ReLU and exponential. Leveraging the Neural Tangent Kernel (NTK) framework, our analysis reveals that the normalization effect of the softmax function leads to a good perturbation property of the induced NTK matrix, resulting in a good convex region of the loss landscape. Consequently, softmax neural networks can learn the target function in the over-parametrization regime. To demonstrate the broad applicability of our theoretical findings, we apply them to the task of learning score estimation functions in diffusion models, a promising approach for generative modeling. Our analysis shows that gradient-based algorithms can learn the score function with a provable accuracy. Our work provides a deeper understanding of the effectiveness of softmax neural networks and their potential in various domains, paving the way for further advancements in natural language processing and beyond.

ML-44-标题: Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth

链接: https://arxiv.org/abs/2405.03248
作者: Ying Zhuansun, Dandan Li, Xiaohong Huang, Caijun Sun
备注:

点击查看摘要

Abstract:Federated learning can train models without directly providing local data to the server. However, the frequent updating of the local model brings the problem of large communication overhead. Recently, scholars have achieved the communication efficiency of federated learning mainly by model compression. But they ignore two problems: 1) network state of each client changes dynamically; 2) network state among clients is not the same. The clients with poor bandwidth update local model slowly, which leads to low efficiency. To address this challenge, we propose a communication-efficient federated learning algorithm with adaptive compression under dynamic bandwidth (called AdapComFL). Concretely, each client performs bandwidth awareness and bandwidth prediction. Then, each client adaptively compresses its local model via the improved sketch mechanism based on his predicted bandwidth. Further, the server aggregates sketched models with different sizes received. To verify the effectiveness of the proposed method, the experiments are based on real bandwidth data which are collected from the network topology we build, and benchmark datasets which are obtained from open repositories. We show the performance of AdapComFL algorithm, and compare it with existing algorithms. The experimental results show that our AdapComFL achieves more efficient communication as well as competitive accuracy compared to existing algorithms.

ML-45-标题: Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition

链接: https://arxiv.org/abs/2405.03244
作者: Nishant Suresh Aswani, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
备注:

点击查看摘要

Abstract:Continual learning (CL) has spurred the development of several methods aimed at consolidating previous knowledge across sequential learning. Yet, the evaluations of these methods have primarily focused on the final output, such as changes in the accuracy of predicted classes, overlooking the issue of representational forgetting within the model. In this paper, we propose a novel representation-based evaluation framework for CL models. This approach involves gathering internal representations from throughout the continual learning process and formulating three-dimensional tensors. The tensors are formed by stacking representations, such as layer activations, generated from several inputs and model `snapshots’, throughout the learning process. By conducting tensor component analysis (TCA), we aim to uncover meaningful patterns about how the internal representations evolve, expecting to highlight the merits or shortcomings of examined CL strategies. We conduct our analyses across different model architectures and importance-based continual learning strategies, with a curated task selection. While the results of our approach mirror the difference in performance of various CL strategies, we found that our methodology did not directly highlight specialized clusters of neurons, nor provide an immediate understanding the evolution of filters. We believe a scaled down version of our approach will provide insight into the benefits and pitfalls of using TCA to study continual learning dynamics.

ML-46-标题: Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series: A UK Biobank Study

链接: https://arxiv.org/abs/2405.03239
作者: Shuhao Mei, Yuxi Zhou, Jiahao Xu, Yuxuan Wan, Shan Cao, Qinghao Zhao, Shijia Geng, Junqing Xie, Shenda Hong
备注:

点击查看摘要

Abstract:Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung condition that causes airflow obstruction. The existing methods can only detect patients who already have COPD based on obvious features shown in the spirogram (In this article, the spirogram specifically involves measuring Volume-Flow curve time series). Early prediction of COPD risk is vital for monitoring COPD disease progression, slowing it down, or even preventing its onset. However, these methods fail to early predict an individual’s probability of COPD in the future based on subtle features in the spirogram. To address this gap, for the first time, we propose DeepSpiro, a method based on deep learning for early prediction of future COPD risk. DeepSpiro consists of four parts. First, we construct Volume-Flow curves guided by Time-Volume instability smoothing (SpiroSmoother) to enhance the stability of the original Volume-Flow curves precisely. Second, we extract critical features from the evolution of varied-length key patches (SpiroEncoder) to capture the key temporal evolution from original high-dimensional dynamic sequences to a unified low-dimensional temporal representation. Third, we explain the model based on temporal attention and heterogeneous feature fusion (SpiroExplainer), which integrates information from heterogeneous data such as spirogram and demographic information. Fourth, we predict the risk of COPD based on the evolution of key patch concavity (SpiroPredictor), enabling accurate prediction of the risk of disease in high-risk patients who are not yet diagnosed, for up to 1, 2, 3, 4, 5 years, and beyond. We conduct experiments on the UK Biobank dataset. Results show that DeepSpiro achieves an AUC value of 0.8328 in the task of detecting COPD. In early prediction tasks, high-risk and low-risk groups show significant differences in the future, with a p-value of <0.001.

ML-47-标题: Federated Reinforcement Learning with Constraint Heterogeneity

链接: https://arxiv.org/abs/2405.03236
作者: Hao Jin, Liangyu Zhang, Zhihua Zhang
备注:

点击查看摘要

Abstract:We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while N training agents are located in N different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce N local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, FedNPG and FedPPO. We show that FedNPG achieves global convergence with an \tildeO(1/\sqrtT) rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.

ML-48-标题: A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series

链接: https://arxiv.org/abs/2405.03234
作者: Ziquan Deng, Xiwei Xuan, Kwan-Liu Ma, Zhaodan Kong
备注: The manuscript is currently under review

点击查看摘要

Abstract:Time series anomaly detection is a critical machine learning task for numerous applications, such as finance, healthcare, and industrial systems. However, even high-performed models may exhibit potential issues such as biases, leading to unreliable outcomes and misplaced confidence. While model explanation techniques, particularly visual explanations, offer valuable insights to detect such issues by elucidating model attributions of their decision, many limitations still exist – They are primarily instance-based and not scalable across dataset, and they provide one-directional information from the model to the human side, lacking a mechanism for users to address detected issues. To fulfill these gaps, we introduce HILAD, a novel framework designed to foster a dynamic and bidirectional collaboration between humans and AI for enhancing anomaly detection models in time series. Through our visual interface, HILAD empowers domain experts to detect, interpret, and correct unexpected model behaviors at scale. Our evaluation with two time series datasets and user studies demonstrates the effectiveness of HILAD in fostering a deeper human understanding, immediate corrective actions, and the reliability enhancement of models.

ML-49-标题: TED: Accelerate Model Training by Internal Generalization

链接: https://arxiv.org/abs/2405.03228
作者: Jinying Xiao, Ping Li, Jie Nie
备注:

点击查看摘要

Abstract:Large language models have demonstrated strong performance in recent years, but the high cost of training drives the need for efficient methods to compress dataset sizes. We propose TED pruning, a method that addresses the challenge of overfitting under high pruning ratios by quantifying the model’s ability to improve performance on pruned data while fitting retained data, known as Internal Generalization (IG). TED uses an optimization objective based on Internal Generalization Distance (IGD), measuring changes in IG before and after pruning to align with true generalization performance and achieve implicit regularization. The IGD optimization objective was verified to allow the model to achieve the smallest upper bound on generalization error. The impact of small mask fluctuations on IG is studied through masks and Taylor approximation, and fast estimation of IGD is enabled. In analyzing continuous training dynamics, the prior effect of IGD is validated, and a progressive pruning strategy is proposed. Experiments on image classification, natural language understanding, and large language model fine-tuning show TED achieves lossless performance with 60-70% of the data. Upon acceptance, our code will be made publicly available.

ML-50-标题: Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting

链接: https://arxiv.org/abs/2405.03199
作者: Nannan Bian, Minhong Zhu, Li Chen, Weiran Cai
备注:

点击查看摘要

Abstract:Deep learning methods have been exerting their strengths in long-term time series forecasting. However, they often struggle to strike a balance between expressive power and computational efficiency. Here, we propose the Coarsened Perceptron Network (CP-Net), a novel architecture that efficiently enhances the predictive capability of MLPs while maintains a linear computational complexity. It utilizes a coarsening strategy as the backbone that leverages two-stage convolution-based sampling blocks. Based purely on convolution, they provide the functionality of extracting short-term semantic and contextual patterns, which is relatively deficient in the global point-wise projection of the MLP layer. With the architectural simplicity and low runtime, our experiments on seven time series forecasting benchmarks demonstrate that CP-Net achieves an improvement of 4.1% compared to the SOTA method. The model further shows effective utilization of the exposed information with a consistent improvement as the look-back window expands.

ML-51-标题: QuadraNet V2: Efficient and Sustainable Training of High-Order Neural Networks with Quadratic Adaptation

链接: https://arxiv.org/abs/2405.03192
作者: Chenhui Xu, Xinyao Wang, Fuxun Yu, JInjun Xiong, Xiang Chen
备注:

点击查看摘要

Abstract:Machine learning is evolving towards high-order models that necessitate pre-training on extensive datasets, a process associated with significant overheads. Traditional models, despite having pre-trained weights, are becoming obsolete due to architectural differences that obstruct the effective transfer and initialization of these weights. To address these challenges, we introduce a novel framework, QuadraNet V2, which leverages quadratic neural networks to create efficient and sustainable high-order learning models. Our method initializes the primary term of the quadratic neuron using a standard neural network, while the quadratic term is employed to adaptively enhance the learning of data non-linearity or shifts. This integration of pre-trained primary terms with quadratic terms, which possess advanced modeling capabilities, significantly augments the information characterization capacity of the high-order network. By utilizing existing pre-trained weights, QuadraNet V2 reduces the required GPU hours for training by 90% to 98.4% compared to training from scratch, demonstrating both efficiency and effectiveness.

ML-52-标题: Hyperbolic Geometric Latent Diffusion Model for Graph Generation ICML2024

链接: https://arxiv.org/abs/2405.03188
作者: Xingcheng Fu, Yisen Gao, Yuecen Wei, Qingyun Sun, Hao Peng, Jianxin Li, Xianxian Li
备注: Accepted by the 41st International Conference on Machine Learning (ICML 2024)

点击查看摘要

Abstract:Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

ML-53-标题: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner

链接: https://arxiv.org/abs/2405.03185
作者: Tong Nie, Guoyang Qin, Wei Ma, Jian Sun
备注:

点击查看摘要

Abstract:Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employed to directly map coordinates to traffic variables. To unravel the entangled spatial-temporal interactions, the variability is decomposed into separate processes. We further enable modeling in irregular spaces such as sensor graphs using spectral embedding. Through continuous representations, our approach enables the modeling of a variety of STTD with a unified input, thereby serving as a generalized learner of the underlying traffic dynamics. It is also shown that it can learn implicit low-rank priors and smoothness regularization from the data, making it versatile for learning different dominating data patterns. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales. Empirical results not only indicate that our model has significant superiority over conventional low-rank models, but also highlight that the versatility of the approach extends to different data domains, output resolutions, and network topologies. Comprehensive model analyses provide further insight into the inductive bias of STTD. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks.

ML-54-标题: Decentralized Online Learning in General-Sum Stackelberg Games UAI2024

链接: https://arxiv.org/abs/2405.03158
作者: Yaolong Yu, Haipeng Chen
备注: Accepted for the 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)

点击查看摘要

Abstract:We study an online learning problem in general-sum Stackelberg games, where players act in a decentralized and strategic manner. We study two settings depending on the type of information for the follower: (1) the limited information setting where the follower only observes its own reward, and (2) the side information setting where the follower has extra side information about the leader’s reward. We show that for the follower, myopically best responding to the leader’s action is the best strategy for the limited information setting, but not necessarily so for the side information setting – the follower can manipulate the leader’s reward signals with strategic actions, and hence induce the leader’s strategy to converge to an equilibrium that is better off for itself. Based on these insights, we study decentralized online learning for both players in the two settings. Our main contribution is to derive last-iterate convergence and sample complexity results in both settings. Notably, we design a new manipulation strategy for the follower in the latter setting, and show that it has an intrinsic advantage against the best response strategy. Our theories are also supported by empirical results.

ML-55-标题: TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning ICML2024

链接: https://arxiv.org/abs/2405.03140
作者: Xiwen Chen, Peijie Qiu, Wenhui Zhu, Huayu Li, Hao Wang, Aristeidis Sotiras, Yalin Wang, Abolfazl Razi
备注: Accepted by ICML2024

点击查看摘要

Abstract:Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods often rely on supervised learning, which does not fully account for the sparsity and locality of patterns in time series data (e.g., diseases-related anomalous points in ECG). To address this challenge, we formally reformulate MTSC as a weakly supervised problem, introducing a novel multiple-instance learning (MIL) framework for better localization of patterns of interest and modeling time dependencies within time series. Our novel approach, TimeMIL, formulates the temporal correlation and ordering within a time-aware MIL pooling, leveraging a tokenized transformer with a specialized learnable wavelet positional token. The proposed method surpassed 26 recent state-of-the-art methods, underscoring the effectiveness of the weakly supervised TimeMIL in MTSC.

ML-56-标题: WDMoE: Wireless Distributed Large Language Models with Mixture of Experts

链接: https://arxiv.org/abs/2405.03131
作者: Nan Xue, Yaping Sun, Zhiyong Chen, Meixia Tao, Xiaodong Xu, Liang Qian, Shuguang Cui, Ping Zhang
备注: submitted to IEEE conference

点击查看摘要

Abstract:Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless communications can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, deploying LLMs collaboratively across edge servers of base station (BS) and mobile devices in the wireless communications system. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at BS, while distributing the expert networks across the devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

ML-57-标题: Loss Jump During Loss Switch in Solving PDEs with Neural Networks

链接: https://arxiv.org/abs/2405.03095
作者: Zhiwei Wang, Lulu Zhang, Zhongwang Zhang, Zhi-Qin John Xu
备注:

点击查看摘要

Abstract:Using neural networks to solve partial differential equations (PDEs) is gaining popularity as an alternative approach in the scientific computing community. Neural networks can integrate different types of information into the loss function. These include observation data, governing equations, and variational forms, etc. These loss functions can be broadly categorized into two types: observation data loss directly constrains and measures the model output, while other loss functions indirectly model the performance of the network, which can be classified as model loss. However, this alternative approach lacks a thorough understanding of its underlying mechanisms, including theoretical foundations and rigorous characterization of various phenomena. This work focuses on investigating how different loss functions impact the training of neural networks for solving PDEs. We discover a stable loss-jump phenomenon: when switching the loss function from the data loss to the model loss, which includes different orders of derivative information, the neural network solution significantly deviates from the exact solution immediately. Further experiments reveal that this phenomenon arises from the different frequency preferences of neural networks under different loss functions. We theoretically analyze the frequency preference of neural networks under model loss. This loss-jump phenomenon provides a valuable perspective for examining the underlying mechanisms of neural networks in solving PDEs.

ML-58-标题: Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition

链接: https://arxiv.org/abs/2405.03089
作者: Xitong Zhang, Ismail R. Alkhouri, Rongrong Wang
备注:

点击查看摘要

Abstract:Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNNs pose a challenge for deploying these trained models on resource-limited devices. Therefore, a plethora of compression and pruning techniques have been proposed in recent years. Low-rank decomposition techniques are among the approaches most utilized to address this problem. Compared to post-training compression, compression-promoted training is still under-explored. In this paper, we present a theoretically-justified novel approach, termed Low-Rank Induced Training (LoRITa), that promotes low-rankness through the composition of linear layers and compresses by using singular value truncation. This is achieved without the need to change the structure at inference time or require constrained and/or additional optimization, other than the standard weight decay regularization. Moreover, LoRITa eliminates the need to (i) initialize with pre-trained models and (ii) specify rank selection prior to training. Our experimental results (i) demonstrate the effectiveness of our approach using MNIST on Fully Connected Networks, CIFAR10 on Vision Transformers, and CIFAR10/100 on Convolutional Neural Networks, and (ii) illustrate that we achieve either competitive or SOTA results when compared to leading structured pruning methods in terms of FLOPs and parameters drop.

ML-59-标题: Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning ICML2024

链接: https://arxiv.org/abs/2405.03082
作者: Tianchen Zhou, FNU Hairi, Haibo Yang, Jia Liu, Tian Tong, Fan Yang, Michinari Momma, Yan Gao
备注: Accepted in ICML 2024

点击查看摘要

Abstract:Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This paper tackles the multi-objective reinforcement learning (MORL) problem and introduces an innovative actor-critic algorithm named MOAC which finds a policy by iteratively making trade-offs among conflicting reward signals. Notably, we provide the first analysis of finite-time Pareto-stationary convergence and corresponding sample complexity in both discounted and average reward settings. Our approach has two salient features: (a) MOAC mitigates the cumulative estimation bias resulting from finding an optimal common gradient descent direction out of stochastic samples. This enables provable convergence rate and sample complexity guarantees independent of the number of objectives; (b) With proper momentum coefficient, MOAC initializes the weights of individual policy gradients using samples from the environment, instead of manual initialization. This enhances the practicality and robustness of our algorithm. Finally, experiments conducted on a real-world dataset validate the effectiveness of our proposed method.

ML-60-标题: AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection

链接: https://arxiv.org/abs/2405.03075
作者: Aditya Singh, Pavan Reddy
备注: 12 pages, 6 figures, accepted as Short paper at HCII 2024 (this https URL)

点击查看摘要

Abstract:Anomaly detection, a critical facet in data analysis, involves identifying patterns that deviate from expected behavior. This research addresses the complexities inherent in anomaly detection, exploring challenges and adapting to sophisticated malicious activities. With applications spanning cybersecurity, healthcare, finance, and surveillance, anomalies often signify critical information or potential threats. Inspired by the success of Anomaly Generative Adversarial Network (AnoGAN) in image domains, our research extends its principles to tabular data. Our contributions include adapting AnoGAN’s principles to a new domain and promising advancements in detecting previously undetectable anomalies. This paper delves into the multifaceted nature of anomaly detection, considering the dynamic evolution of normal behavior, context-dependent anomaly definitions, and data-related challenges like noise and imbalances.

ML-61-标题: RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation ICML2024

链接: https://arxiv.org/abs/2405.03064
作者: Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang, Xinyu Xing
备注: Accepted by ICML 2024

点击查看摘要

Abstract:Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, especially with sparse rewards, remains a significant challenge. The training of a DRL agent can be often trapped in a bottleneck without further progress. In this paper, we propose RICE, an innovative refining scheme for reinforcement learning that incorporates explanation methods to break through the training bottlenecks. The high-level idea of RICE is to construct a new initial state distribution that combines both the default initial states and critical states identified through explanation methods, thereby encouraging the agent to explore from the mixed initial states. Through careful design, we can theoretically guarantee that our refining scheme has a tighter sub-optimality bound. We evaluate RICE in various popular RL environments and real-world applications. The results demonstrate that RICE significantly outperforms existing refining schemes in enhancing agent performance.

ML-62-标题: Tree-based Ensemble Learning for Out-of-distribution Detection

链接: https://arxiv.org/abs/2405.03060
作者: Zhaiming Shen, Menglun Wang, Guang Cheng, Ming-Jun Lai, Lin Mu, Ruihao Huang, Qi Liu, Hao Zhu
备注:

点击查看摘要

Abstract:Being able to successfully determine whether the testing samples has similar distribution as the training samples is a fundamental question to address before we can safely deploy most of the machine learning models into practice. In this paper, we propose TOOD detection, a simple yet effective tree-based out-of-distribution (TOOD) detection mechanism to determine if a set of unseen samples will have similar distribution as of the training samples. The TOOD detection mechanism is based on computing pairwise hamming distance of testing samples’ tree embeddings, which are obtained by fitting a tree-based ensemble model through in-distribution training samples. Our approach is interpretable and robust for its tree-based nature. Furthermore, our approach is efficient, flexible to various machine learning tasks, and can be easily generalized to unsupervised setting. Extensive experiments are conducted to show the proposed method outperforms other state-of-the-art out-of-distribution detection methods in distinguishing the in-distribution from out-of-distribution on various tabular, image, and text data.

ML-63-标题: Active Preference Learning for Ordering Items In- and Out-of-sample

链接: https://arxiv.org/abs/2405.03059
作者: Herman Bergström, Emil Carlsson, Devdatt Dubhashi, Fredrik D. Johansson
备注:

点击查看摘要

Abstract:Learning an ordering of items based on noisy pairwise comparisons is useful when item-specific labels are difficult to assign, for example, when annotators have to make subjective assessments. Algorithms have been proposed for actively sampling comparisons of items to minimize the number of annotations necessary for learning an accurate ordering. However, many ignore shared structure between items, treating them as unrelated, limiting sample efficiency and precluding generalization to new items. In this work, we study active learning with pairwise preference feedback for ordering items with contextual attributes, both in- and out-of-sample. We give an upper bound on the expected ordering error incurred by active learning strategies under a logistic preference model, in terms of the aleatoric and epistemic uncertainty in comparisons, and propose two algorithms designed to greedily minimize this bound. We evaluate these algorithms in two realistic image ordering tasks, including one with comparisons made by human annotators, and demonstrate superior sample efficiency compared to non-contextual ranking approaches and active preference learning baselines.

ML-64-标题: Convolutional Learning on Directed Acyclic Graphs

链接: https://arxiv.org/abs/2405.03056
作者: Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
备注:

点击查看摘要

Abstract:We develop a novel convolutional architecture tailored for learning from data defined over directed acyclic graphs (DAGs). DAGs can be used to model causal relationships among variables, but their nilpotent adjacency matrices pose unique challenges towards developing DAG signal processing and machine learning tools. To address this limitation, we harness recent advances offering alternative definitions of causal shifts and convolutions for signals on DAGs. We develop a novel convolutional graph neural network that integrates learnable DAG filters to account for the partial ordering induced by the graph topology, thus providing valuable inductive bias to learn effective representations of DAG-supported data. We discuss the salient advantages and potential limitations of the proposed DAG convolutional network (DCN) and evaluate its performance on two learning tasks using synthetic data: network diffusion estimation and source identification. DCN compares favorably relative to several baselines, showcasing its promising potential.

ML-65-标题: A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspective

链接: https://arxiv.org/abs/2405.03052
作者: Alberto Caron, Chris Hicks, Vasilios Mavroudis
备注:

点击查看摘要

Abstract:We study the problem of efficiently detecting Out-of-Distribution (OOD) samples at test time in supervised and unsupervised learning contexts. While ML models are typically trained under the assumption that training and test data stem from the same distribution, this is often not the case in realistic settings, thus reliably detecting distribution shifts is crucial at deployment. We re-formulate the OOD problem under the lenses of statistical testing and then discuss conditions that render the OOD problem identifiable in statistical terms. Building on this framework, we study convergence guarantees of an OOD test based on the Wasserstein distance, and provide a simple empirical evaluation.

ML-66-标题: Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints

链接: https://arxiv.org/abs/2405.03005
作者: Siow Meng Low, Akshat Kumar
备注:

点击查看摘要

Abstract:In safe Reinforcement Learning (RL), safety cost is typically defined as a function dependent on the immediate state and actions. In practice, safety constraints can often be non-Markovian due to the insufficient fidelity of state representation, and safety cost may not be known. We therefore address a general setting where safety labels (e.g., safe or unsafe) are associated with state-action trajectories. Our key contributions are: first, we design a safety model that specifically performs credit assignment to assess contributions of partial state-action trajectories on safety. This safety model is trained using a labeled safety dataset. Second, using RL-as-inference strategy we derive an effective algorithm for optimizing a safe policy using the learned safety model. Finally, we devise a method to dynamically adapt the tradeoff coefficient between reward maximization and safety compliance. We rewrite the constrained optimization problem into its dual problem and derive a gradient-based method to dynamically adjust the tradeoff coefficient during training. Our empirical results demonstrate that this approach is highly scalable and able to satisfy sophisticated non-Markovian safety constraints.

ML-67-标题: Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation

链接: https://arxiv.org/abs/2405.02969
作者: Banruo Liu, Mubarak Adetunji Ojewale, Yuhan Ding, Marco Canini
备注:

点击查看摘要

Abstract:We propose NeuronaBox, a flexible, user-friendly, and high-fidelity approach to emulate DNN training workloads. We argue that to accurately observe performance, it is possible to execute the training workload on a subset of real nodes and emulate the networked execution environment along with the collective communication operations. Initial results from a proof-of-concept implementation show that NeuronaBox replicates the behavior of actual systems with high accuracy, with an error margin of less than 1% between the emulated measurements and the real system.

ML-68-标题: CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning

链接: https://arxiv.org/abs/2405.02968
作者: Hirokazu Ishida, Naoki Hiraoka, Kei Okada, Masayuki Inaba
备注:

点击查看摘要

Abstract:Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the trade-off between plannability and speed observed in global (e.g. sampling-based) and local (e.g. optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.

ML-69-标题: Analysis of the Identifying Regulation with Adversarial Surrogates Algorithm

链接: https://arxiv.org/abs/2405.02953
作者: Ron Teichner, Ron Meir, Michael Margaliot
备注:

点击查看摘要

Abstract:Given a time-series of noisy measured outputs of a dynamical system z[k], k=1…N, the Identifying Regulation with Adversarial Surrogates (IRAS) algorithm aims to find a non-trivial first integral of the system, namely, a scalar function g() such that g(z[i]) = g(z[j]), for all i,j. IRAS has been suggested recently and was used successfully in several learning tasks in models from biology and physics. Here, we give the first rigorous analysis of this algorithm in a specific setting. We assume that the observations admit a linear first integral and that they are contaminated by Gaussian noise. We show that in this case the IRAS iterations are closely related to the self-consistent-field (SCF) iterations for solving a generalized Rayleigh quotient minimization problem. Using this approach, we derive several sufficient conditions guaranteeing local convergence of IRAS to the correct first integral.

ML-70-标题: Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving

链接: https://arxiv.org/abs/2405.02952
作者: Sohei Arisaka, Qianxiao Li
备注:

点击查看摘要

Abstract:Scientific computing is an essential tool for scientific discovery and engineering design, and its computational cost is always a main concern in practice. To accelerate scientific computing, it is a promising approach to use machine learning (especially meta-learning) techniques for selecting hyperparameters of traditional numerical methods. There have been numerous proposals to this direction, but many of them require automatic-differentiable numerical methods. However, in reality, many practical applications still depend on well-established but non-automatic-differentiable legacy codes, which prevents practitioners from applying the state-of-the-art research to their own problems. To resolve this problem, we propose a non-intrusive methodology with a novel gradient estimation technique to combine machine learning and legacy numerical codes without any modification. We theoretically and numerically show the advantage of the proposed method over other baselines and present applications of accelerating established non-automatic-differentiable numerical solvers implemented in PETSc, a widely used open-source numerical software library.

ML-71-标题: On the tractability of SHAP explanations under Markovian distributions ICML’24

链接: https://arxiv.org/abs/2405.02936
作者: Reda Marzouk, Colin de La Higuera
备注: Accepted at ICML’24 (This version is a pre-print)

点击查看摘要

Abstract:Thanks to its solid theoretical foundation, the SHAP framework is arguably one the most widely utilized frameworks for local explainability of ML models. Despite its popularity, its exact computation is known to be very challenging, proven to be NP-Hard in various configurations. Recent works have unveiled positive complexity results regarding the computation of the SHAP score for specific model families, encompassing decision trees, random forests, and some classes of boolean circuits. Yet, all these positive results hinge on the assumption of feature independence, often simplistic in real-world scenarios. In this article, we investigate the computational complexity of the SHAP score by relaxing this assumption and introducing a Markovian perspective. We show that, under the Markovian assumption, computing the SHAP score for the class of Weighted automata, Disjoint DNFs and Decision Trees can be performed in polynomial time, offering a first positive complexity result for the problem of SHAP score computation that transcends the limitations of the feature independence assumption.

ML-72-标题: Predicting Open-Hole Laminates Failure Using Support Vector Machines With Classical and Quantum Kernels

链接: https://arxiv.org/abs/2405.02903
作者: Giorgio Tosti Balducci, Boyang Chen, Matthias Möller, Marc Gerritsma, Roeland De Breuker
备注:

点击查看摘要

Abstract:Modeling open hole failure of composites is a complex task, consisting in a highly nonlinear response with interacting failure modes. Numerical modeling of this phenomenon has traditionally been based on the finite element method, but requires to tradeoff between high fidelity and computational cost. To mitigate this shortcoming, recent work has leveraged machine learning to predict the strength of open hole composite specimens. Here, we also propose using data-based models but to tackle open hole composite failure from a classification point of view. More specifically, we show how to train surrogate models to learn the ultimate failure envelope of an open hole composite plate under in-plane loading. To achieve this, we solve the classification problem via support vector machine (SVM) and test different classifiers by changing the SVM kernel function. The flexibility of kernel-based SVM also allows us to integrate the recently developed quantum kernels in our algorithm and compare them with the standard radial basis function (RBF) kernel. Finally, thanks to kernel-target alignment optimization, we tune the free parameters of all kernels to best separate safe and failure-inducing loading states. The results show classification accuracies higher than 90% for RBF, especially after alignment, followed closely by the quantum kernel classifiers.

ML-73-标题: FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients IJCAI

链接: https://arxiv.org/abs/2405.02881
作者: Zhuohua Li, Maoli Liu, John C.S. Lui
备注: Accepted in the 33rd International Joint Conference on Artificial Intelligence (IJCAI), 2024

点击查看摘要

Abstract:Conversational recommender systems have emerged as a potent solution for efficiently eliciting user preferences. These systems interactively present queries associated with “key terms” to users and leverage user feedback to estimate user preferences more efficiently. Nonetheless, most existing algorithms adopt a centralized approach. In this paper, we introduce FedConPE, a phase elimination-based federated conversational bandit algorithm, where M agents collaboratively solve a global contextual linear bandit problem with the help of a central server while ensuring secure data management. To effectively coordinate all the clients and aggregate their collected data, FedConPE uses an adaptive approach to construct key terms that minimize uncertainty across all dimensions in the feature space. Furthermore, compared with existing federated linear bandit algorithms, FedConPE offers improved computational and communication efficiency as well as enhanced privacy protections. Our theoretical analysis shows that FedConPE is minimax near-optimal in terms of cumulative regret. We also establish upper bounds for communication costs and conversation frequency. Comprehensive evaluations demonstrate that FedConPE outperforms existing conversational bandit algorithms while using fewer conversations.

ML-74-标题: Exploring the Improvement of Evolutionary Computation via Large Language Models GECCO2024

链接: https://arxiv.org/abs/2405.02876
作者: Jinyu Cai, Jinglue Xu, Jialong Li, Takuto Ymauchi, Hitoshi Iba, Kenji Tei
备注: accepted by GECCO 2024

点击查看摘要

Abstract:Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains. However, as the complexity of problems increases, the limitations of EC have become more apparent. The advent of large language models (LLMs) has not only transformed natural language processing but also extended their capabilities to diverse fields. By harnessing LLMs’ vast knowledge and adaptive capabilities, we provide a forward-looking overview of potential improvements LLMs can bring to EC, focusing on the algorithms themselves, population design, and additional enhancements. This presents a promising direction for future research at the intersection of LLMs and EC.

ML-75-标题: Data-Efficient Molecular Generation with Hierarchical Textual Inversion

链接: https://arxiv.org/abs/2405.02845
作者: Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin
备注:

点击查看摘要

Abstract:Developing an effective molecular generation framework even with a limited number of molecules is often important for its practical deployment, e.g., drug discovery, since acquiring task-related molecular data requires expensive and time-consuming experimental costs. To tackle this issue, we introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method. HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution. We propose to use multi-level embeddings to reflect such hierarchical features based on the adoption of the recent textual inversion technique in the visual domain, which achieves data-efficient image generation. Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution. We then generate molecules based on the interpolation of the multi-level token embeddings. Extensive experiments demonstrate the superiority of HI-Mol with notable data-efficiency. For instance, on QM9, HI-Mol outperforms the prior state-of-the-art method with 50x less training data. We also show the effectiveness of molecules generated by HI-Mol in low-shot molecular property prediction.

ML-76-标题: IceFormer: Accelerated Inference with Long-Sequence Transformer s on CPUs

链接: https://arxiv.org/abs/2405.02842
作者: Yuzhen Mao, Martin Ester, Ke Li
备注:

点击查看摘要

Abstract:One limitation of existing Transformer-based models is that they cannot handle very long sequences as input since their self-attention operations exhibit quadratic time and space complexity. This problem becomes especially acute when Transformers are deployed on hardware platforms equipped only with CPUs. To address this issue, we propose a novel method for accelerating self-attention at inference time that works with pretrained Transformer models out-of-the-box without requiring retraining. We experiment using our method to accelerate various long-sequence Transformers, including a leading LLaMA 2-based LLM, on various benchmarks and demonstrate a greater speedup of 2.73x - 7.63x while retaining 98.6% - 99.6% of the accuracy of the original pretrained models. The code is available on our project website at this https URL.

ML-77-标题: Trojans in Large Language Models of Code: A Critical Review through a Trigger-Based Taxonomy

链接: https://arxiv.org/abs/2405.02828
作者: Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed, Bowen Xu, Premkumar Devanbu, Mohammad Amin Alipour
备注: arXiv admin note: substantial text overlap with arXiv:2305.03803

点击查看摘要

Abstract:Large language models (LLMs) have provided a lot of exciting new capabilities in software development. However, the opaque nature of these models makes them difficult to reason about and inspect. Their opacity gives rise to potential security risks, as adversaries can train and deploy compromised models to disrupt the software development process in the victims’ organization. This work presents an overview of the current state-of-the-art trojan attacks on large language models of code, with a focus on triggers – the main design point of trojans – with the aid of a novel unifying trigger taxonomy framework. We also aim to provide a uniform definition of the fundamental concepts in the area of trojans in Code LLMs. Finally, we draw implications of findings on how code models learn on trigger design.

ML-78-标题: Sim2Real Transfer for Audio-Visual Navigation with Frequency-Adaptive Acoustic Field Prediction

链接: https://arxiv.org/abs/2405.02821
作者: Changan Chen, Jordi Ramos, Anshul Tomar, Kristen Grauman
备注:

点击查看摘要

Abstract:Sim2real transfer has received increasing attention lately due to the success of learning robotic tasks in simulation end-to-end. While there has been a lot of progress in transferring vision-based navigation policies, the existing sim2real strategy for audio-visual navigation performs data augmentation empirically without measuring the acoustic gap. The sound differs from light in that it spans across much wider frequencies and thus requires a different solution for sim2real. We propose the first treatment of sim2real for audio-visual navigation by disentangling it into acoustic field prediction (AFP) and waypoint navigation. We first validate our design choice in the SoundSpaces simulator and show improvement on the Continuous AudioGoal navigation benchmark. We then collect real-world data to measure the spectral difference between the simulation and the real world by training AFP models that only take a specific frequency subband as input. We further propose a frequency-adaptive strategy that intelligently selects the best frequency band for prediction based on both the measured spectral difference and the energy distribution of the received audio, which improves the performance on the real data. Lastly, we build a real robot platform and show that the transferred policy can successfully navigate to sounding objects. This work demonstrates the potential of building intelligent agents that can see, hear, and act entirely from simulation, and transferring them to the real world.

ML-79-标题: Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models ICLR

链接: https://arxiv.org/abs/2405.02805
作者: Ezra Erives, Bowen Jing, Tommi Jaakkola
备注: ICLR AI4DifferentialEqautions In Science workshop 2024

点击查看摘要

Abstract:Approximations in computing model likelihoods with continuous normalizing flows (CNFs) hinder the use of these models for importance sampling of Boltzmann distributions, where exact likelihoods are required. In this work, we present Verlet flows, a class of CNFs on an augmented state-space inspired by symplectic integrators from Hamiltonian dynamics. When used with carefully constructed Taylor-Verlet integrators, Verlet flows provide exact-likelihood generative models which generalize coupled flow architectures from a non-continuous setting while imposing minimal expressivity constraints. On experiments over toy densities, we demonstrate that the variance of the commonly used Hutchinson trace estimator is unsuitable for importance sampling, whereas Verlet flows perform comparably to full autograd trace computations while being significantly faster.

ML-80-标题: Is Flash Attention Stable?

链接: https://arxiv.org/abs/2405.02803
作者: Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
备注:

点击查看摘要

Abstract:Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today’s workloads. Recently, many organizations training state-of-the-art Generative AI models have reported cases of instability during training, often taking the form of loss spikes. Numeric deviation has emerged as a potential cause of this training instability, although quantifying this is especially challenging given the costly nature of training runs. In this work, we develop a principled approach to understanding the effects of numeric deviation, and construct proxies to put observations into context when downstream effects are difficult to quantify. As a case study, we apply this framework to analyze the widely-adopted Flash Attention optimization. We find that Flash Attention sees roughly an order of magnitude more numeric deviation as compared to Baseline Attention at BF16 when measured during an isolated forward pass. We then use a data-driven analysis based on the Wasserstein Distance to provide upper bounds on how this numeric deviation impacts model weights during training, finding that the numerical deviation present in Flash Attention is 2-5 times less significant than low-precision training.

ML-81-标题: Graph as Point Set ICML2024

链接: https://arxiv.org/abs/2405.02795
作者: Xiyuan Wang, Pan Li, Muhan Zhang
备注: ICML 2024

点击查看摘要

Abstract:Graph is a fundamental data structure to model interconnections between entities. Set, on the contrary, stores independent elements. To learn graph representations, current Graph Neural Networks (GNNs) primarily use message passing to encode the interconnections. In contrast, this paper introduces a novel graph-to-set conversion method that bijectively transforms interconnected nodes into a set of independent points and then uses a set encoder to learn the graph representation. This conversion method holds dual significance. Firstly, it enables using set encoders to learn from graphs, thereby significantly expanding the design space of GNNs. Secondly, for Transformer, a specific set encoder, we provide a novel and principled approach to inject graph information losslessly, different from all the heuristic structural/positional encoding methods adopted in previous graph transformers. To demonstrate the effectiveness of our approach, we introduce Point Set Transformer (PST), a transformer architecture that accepts a point set converted from a graph as input. Theoretically, PST exhibits superior expressivity for both short-range substructure counting and long-range shortest path distance tasks compared to existing GNNs. Extensive experiments further validate PST’s outstanding real-world performance. Besides Transformer, we also devise a Deepset-based set encoder, which achieves performance comparable to representative GNNs, affirming the versatility of our graph-to-set method.

ML-82-标题: Confidential and Protected Disease Classifier using Fully Homomorphic Encryption

链接: https://arxiv.org/abs/2405.02790
作者: Aditya Malik, Nalini Ratha, Bharat Yalavarthi, Tilak Sharma, Arjun Kaushik, Charanjit Jutla
备注:

点击查看摘要

Abstract:With the rapid surge in the prevalence of Large Language Models (LLMs), individuals are increasingly turning to conversational AI for initial insights across various domains, including health-related inquiries such as disease diagnosis. Many users seek potential causes on platforms like ChatGPT or Bard before consulting a medical professional for their ailment. These platforms offer valuable benefits by streamlining the diagnosis process, alleviating the significant workload of healthcare practitioners, and saving users both time and money by avoiding unnecessary doctor visits. However, Despite the convenience of such platforms, sharing personal medical data online poses risks, including the presence of malicious platforms or potential eavesdropping by attackers. To address privacy concerns, we propose a novel framework combining FHE and Deep Learning for a secure and private diagnosis system. Operating on a question-and-answer-based model akin to an interaction with a medical practitioner, this end-to-end secure system employs Fully Homomorphic Encryption (FHE) to handle encrypted input data. Given FHE’s computational constraints, we adapt deep neural networks and activation functions to the encryted domain. Further, we also propose a faster algorithm to compute summation of ciphertext elements. Through rigorous experiments, we demonstrate the efficacy of our approach. The proposed framework achieves strict security and privacy with minimal loss in performance.

ML-83-标题: PhilHumans: Benchmarking Machine Learning for Personal Health

链接: https://arxiv.org/abs/2405.02770
作者: Vadim Liventsev, Vivek Kumar, Allmin Pradhap Singh Susaiyah, Zixiu Wu, Ivan Rodin, Asfand Yaar, Simone Baloccu, Marharyta Beraziuk, Sebastiano Battiato, Giovanni Maria Farinella, Aki Härmä, Rim Helaoui, Milan Petkovic, Diego Reforgiato Recupero, Ehud Reiter, Daniele Riboni, Raymond Sterling
备注:

点击查看摘要

Abstract:The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other application areas indicates that strong benchmarks are essential for the development of intelligent systems. We present Personal Health Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a holistic suite of benchmarks for machine learning across different Healthcare settings - talk therapy, diet coaching, emergency care, intensive care, obstetric sonography - as well as different learning settings, such as action anticipation, timeseries modeling, insight mining, language modeling, computer vision, reinforcement learning and program synthesis

ML-84-标题: Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization

链接: https://arxiv.org/abs/2405.02769
作者: Youbang Sun, Tao Liu, P. R. Kumar, Shahin Shahrampour
备注:

点击查看摘要

Abstract:This work focuses on the entropy-regularized independent natural policy gradient (NPG) algorithm in multi-agent reinforcement learning. In this work, agents are assumed to have access to an oracle with exact policy evaluation and seek to maximize their respective independent rewards. Each individual’s reward is assumed to depend on the actions of all the agents in the multi-agent system, leading to a game between agents. We assume all agents make decisions under a policy with bounded rationality, which is enforced by the introduction of entropy regularization. In practice, a smaller regularization implies the agents are more rational and behave closer to Nash policies. On the other hand, agents with larger regularization acts more randomly, which ensures more exploration. We show that, under sufficient entropy regularization, the dynamics of this system converge at a linear rate to the quantal response equilibrium (QRE). Although regularization assumptions prevent the QRE from approximating a Nash equilibrium, our findings apply to a wide range of games, including cooperative, potential, and two-player matrix games. We also provide extensive empirical results on multiple games (including Markov games) as a verification of our theoretical analysis.

ML-85-标题: Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning

链接: https://arxiv.org/abs/2405.02754
作者: Weiye Zhao, Tairan He, Feihan Li, Changliu Liu
备注: submissions to Journal of Artificial Intelligence Research. arXiv admin note: text overlap with arXiv:2308.13140

点击查看摘要

Abstract:Deep reinforcement learning (DRL) has demonstrated remarkable performance in many continuous control tasks. However, a significant obstacle to the real-world application of DRL is the lack of safety guarantees. Although DRL agents can satisfy system safety in expectation through reward shaping, designing agents to consistently meet hard constraints (e.g., safety specifications) at every time step remains a formidable challenge. In contrast, existing work in the field of safe control provides guarantees on persistent satisfaction of hard safety constraints. However, these methods require explicit analytical system dynamics models to synthesize safe control, which are typically inaccessible in DRL settings. In this paper, we present a model-free safe control algorithm, the implicit safe set algorithm, for synthesizing safeguards for DRL agents that ensure provable safety throughout training. The proposed algorithm synthesizes a safety index (barrier certificate) and a subsequent safe control law solely by querying a black-box dynamic function (e.g., a digital twin simulator). Moreover, we theoretically prove that the implicit safe set algorithm guarantees finite time convergence to the safe set and forward invariance for both continuous-time and discrete-time systems. We validate the proposed algorithm on the state-of-the-art Safety Gym benchmark, where it achieves zero safety violations while gaining 95% \pm 9% cumulative reward compared to state-of-the-art safe DRL methods. Furthermore, the resulting algorithm scales well to high-dimensional systems with parallel computing.

ML-86-标题: Sub-goal Distillation : A Method to Improve Small Language Agent s

链接: https://arxiv.org/abs/2405.02749
作者: Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar, Marc-Alexandre Cote
备注:

点击查看摘要

Abstract:While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls constrain their practical utility, especially in long-horizon interactive tasks such as decision-making or in scenarios involving continuous ongoing tasks. To address these constraints, we propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model (770M parameters). Our approach involves constructing a hierarchical agent comprising a planning module, which learns through Knowledge Distillation from an LLM to generate sub-goals, and an execution module, which learns to accomplish these sub-goals using elementary actions. In detail, we leverage an LLM to annotate an oracle path with a sequence of sub-goals towards completing a goal. Subsequently, we utilize this annotated data to fine-tune both the planning and execution modules. Importantly, neither module relies on real-time access to an LLM during inference, significantly reducing the overall cost associated with LLM interactions to a fixed cost. In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7% (absolute). Our analysis highlights the efficiency of our approach compared to other LLM-based methods. Our code and annotated data for distillation can be found on GitHub.

ML-87-标题: Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation ICML2024

链接: https://arxiv.org/abs/2405.02745
作者: Haibo Yang, Peiwen Qiu, Prashant Khanduri, Minghong Fang, Jia Liu
备注: Accepted in ICML2024

点击查看摘要

Abstract:Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL training (aka incomplete client participation) due to a myriad of system heterogeneity factors. A popular approach to mitigate impacts of incomplete client participation is the server-assisted federated learning (SA-FL) framework, where the server is equipped with an auxiliary dataset. However, despite SA-FL has been empirically shown to be effective in addressing the incomplete client participation problem, there remains a lack of theoretical understanding for SA-FL. Meanwhile, the ramifications of incomplete client participation in conventional FL are also poorly understood. These theoretical gaps motivate us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is \em not PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under \em incomplete client participation, we propose the \mathsfSAFARI (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show \mathsfSAFARI significantly improves the performance under incomplete client participation.

ML-88-标题: Systematic Review : Anomaly Detection in Connected and Autonomous Vehicles

链接: https://arxiv.org/abs/2405.02731
作者: J. R. V. Solaas, N. Tuptuk, E. Mariconti
备注: 17 pages, 2 tables, 5 figures

点击查看摘要

Abstract:This systematic review focuses on anomaly detection for connected and autonomous vehicles. The initial database search identified 2160 articles, of which 203 were included in this review after rigorous screening and assessment. This study revealed that the most commonly used Artificial Intelligence (AI) algorithms employed in anomaly detection are neural networks like LSTM, CNN, and autoencoders, alongside one-class SVM. Most anomaly-based models were trained using real-world operational vehicle data, although anomalies, such as attacks and faults, were often injected artificially into the datasets. These models were evaluated mostly using five key evaluation metrics: recall, accuracy, precision, F1-score, and false positive rate. The most frequently used selection of evaluation metrics used for anomaly detection models were accuracy, precision, recall, and F1-score. This systematic review presents several recommendations. First, there is a need to incorporate multiple evaluation metrics to provide a comprehensive assessment of the anomaly detection models. Second, only a small proportion of the studies have made their models open source, indicating a need to share models publicly to facilitate collaboration within the research community, and to validate and compare findings effectively. Third, there is a need for benchmarking datasets with predefined anomalies or cyberattacks to test and improve the effectiveness of the proposed anomaly-based detection models. Furthermore, there is a need for future research to investigate the deployment of anomaly detection to a vehicle to assess its performance on the road. There is a notable lack of research done on intrusion detection systems using different protocols to CAN, such as Ethernet and FlexRay.

ML-89-标题: A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems

链接: https://arxiv.org/abs/2405.02726
作者: Andrey Veprikov, Alexander Afanasiev, Anton Khritankov
备注: 21 pages, 15 figures

点击查看摘要

Abstract:Widespread deployment of societal-scale machine learning systems necessitates a thorough understanding of the resulting long-term effects these systems have on their environment, including loss of trustworthiness, bias amplification, and violation of AI safety requirements. We introduce a repeated learning process to jointly describe several phenomena attributed to unintended hidden feedback loops, such as error amplification, induced concept drift, echo chambers and others. The process comprises the entire cycle of obtaining the data, training the predictive model, and delivering predictions to end-users within a single mathematical model. A distinctive feature of such repeated learning setting is that the state of the environment becomes causally dependent on the learner itself over time, thus violating the usual assumptions about the data distribution. We present a novel dynamical systems model of the repeated learning process and prove the limiting set of probability distributions for positive and negative feedback loop modes of the system operation. We conduct a series of computational experiments using an exemplary supervised learning problem on two synthetic data sets. The results of the experiments correspond to the theoretical predictions derived from the dynamical model. Our results demonstrate the feasibility of the proposed approach for studying the repeated learning processes in machine learning systems and open a range of opportunities for further research in the area.

ML-90-标题: Taming Equilibrium Bias in Risk-Sensitive Multi- Agent Reinforcement Learning

链接: https://arxiv.org/abs/2405.02724
作者: Yingjie Fei, Ruitu Xu
备注: 29 pages

点击查看摘要

Abstract:We study risk-sensitive multi-agent reinforcement learning under general-sum Markov games, where agents optimize the entropic risk measure of rewards with possibly diverse risk preferences. We show that using the regret naively adapted from existing literature as a performance metric could induce policies with equilibrium bias that favor the most risk-sensitive agents and overlook the other agents. To address such deficiency of the naive regret, we propose a novel notion of regret, which we call risk-balanced regret, and show through a lower bound that it overcomes the issue of equilibrium bias. Furthermore, we develop a self-play algorithm for learning Nash, correlated, and coarse correlated equilibria in risk-sensitive Markov games. We prove that the proposed algorithm attains near-optimal regret guarantees with respect to the risk-balanced regret.

ML-91-标题: Semi-supervised Symmetric Matrix Factorization with Low-Rank Tensor Representation

链接: https://arxiv.org/abs/2405.02688
作者: Yuheng Jia, Jia-Nan Li, Wenhui Wu, Ran Wang
备注:

点击查看摘要

Abstract:Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ability of SNMF. The previous methods introduce the pairwise constraints from the local perspective, i.e., they either directly refine the similarity matrix element-wisely or restrain the distance of the decomposed vectors in pairs according to the pairwise constraints, which overlook the global perspective, i.e., in the ideal case, the pairwise constraint matrix and the ideal similarity matrix possess the same low-rank structure. To this end, we first propose a novel semi-supervised SNMF model by seeking low-rank representation for the tensor synthesized by the pairwise constraint matrix and a similarity matrix obtained by the product of the embedding matrix and its transpose, which could strengthen those two matrices simultaneously from a global perspective. We then propose an enhanced SNMF model, making the embedding matrix tailored to the above tensor low-rank representation. We finally refine the similarity matrix by the strengthened pairwise constraints. We repeat the above steps to continuously boost the similarity matrix and pairwise constraint matrix, leading to a high-quality embedding matrix. Extensive experiments substantiate the superiority of our method. The code is available at this https URL.

ML-92-标题: FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer

链接: https://arxiv.org/abs/2405.02685
作者: Xin Gao, Xin Yang, Hao Yu, Yan Kang, Tianrui Li
备注:

点击查看摘要

Abstract:Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing methods do not consider the trustworthiness of FCIL, i.e., improving continual utility, privacy, and efficiency simultaneously, which is greatly influenced by catastrophic forgetting and data heterogeneity among clients. To address this issue, we propose FedProK (Federated Prototypical Feature Knowledge Transfer), leveraging prototypical feature as a novel representation of knowledge to perform spatial-temporal knowledge transfer. Specifically, FedProK consists of two components: (1) feature translation procedure on the client side by temporal knowledge transfer from the learned classes and (2) prototypical knowledge fusion on the server side by spatial knowledge transfer among clients. Extensive experiments conducted in both synchronous and asynchronous settings demonstrate that our FedProK outperforms the other state-of-the-art methods in three perspectives of trustworthiness, validating its effectiveness in selectively transferring spatial-temporal knowledge.

ML-93-标题: From Generalization Analysis to Optimization Designs for State Space Models

链接: https://arxiv.org/abs/2405.02670
作者: Fusheng Liu, Qianxiao Li
备注:

点击查看摘要

Abstract:A State Space Model (SSM) is a foundation model in time series analysis, which has recently been shown as an alternative to transformers in sequence modeling. In this paper, we theoretically study the generalization of SSMs and propose improvements to training algorithms based on the generalization results. Specifically, we give a \textitdata-dependent generalization bound for SSMs, showing an interplay between the SSM parameters and the temporal dependencies of the training sequences. Leveraging the generalization bound, we (1) set up a scaling rule for model initialization based on the proposed generalization measure, which significantly improves the robustness of the output value scales on SSMs to different temporal patterns in the sequence data; (2) introduce a new regularization method for training SSMs to enhance the generalization performance. Numerical results are conducted to validate our results.

ML-94-标题: DDE-Find: Learning Delay Differential Equations from Data

链接: https://arxiv.org/abs/2405.02661
作者: Robert Stephany
备注: 42 pages, 19 tables, 8 figures

点击查看摘要

Abstract:Delay Differential Equations (DDEs) are a class of differential equations that can model diverse scientific phenomena. However, identifying the parameters, especially the time delay, that make a DDE’s predictions match experimental results can be challenging. We introduce DDE-Find, a data-driven framework for learning a DDE’s parameters, time delay, and initial condition function. DDE-Find uses an adjoint-based approach to efficiently compute the gradient of a loss function with respect to the model parameters. We motivate and rigorously prove an expression for the gradients of the loss using the adjoint. DDE-Find builds upon recent developments in learning DDEs from data and delivers the first complete framework for learning DDEs from data. Through a series of numerical experiments, we demonstrate that DDE-Find can learn DDEs from noisy, limited data.

ML-95-标题: Generic Multi-modal Representation Learning for Network Traffic Analysis

链接: https://arxiv.org/abs/2405.02649
作者: Luca Gioacchini, Idilio Drago, Marco Mellia, Zied Ben Houidi, Dario Rossi
备注:

点击查看摘要

Abstract:Network traffic analysis is fundamental for network management, troubleshooting, and security. Tasks such as traffic classification, anomaly detection, and novelty discovery are fundamental for extracting operational information from network data and measurements. We witness the shift from deep packet inspection and basic machine learning to Deep Learning (DL) approaches where researchers define and test a custom DL architecture designed for each specific problem. We here advocate the need for a general DL architecture flexible enough to solve different traffic analysis tasks. We test this idea by proposing a DL architecture based on generic data adaptation modules, followed by an integration module that summarises the extracted information into a compact and rich intermediate representation (i.e. embeddings). The result is a flexible Multi-modal Autoencoder (MAE) pipeline that can solve different use cases. We demonstrate the architecture with traffic classification (TC) tasks since they allow us to quantitatively compare results with state-of-the-art solutions. However, we argue that the MAE architecture is generic and can be used to learn representations useful in multiple scenarios. On TC, the MAE performs on par or better than alternatives while avoiding cumbersome feature engineering, thus streamlining the adoption of DL solutions for traffic analysis.

ML-96-标题: Interpretable Multi-View Clustering

链接: https://arxiv.org/abs/2405.02644
作者: Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen
备注: 12 pages,6 figures

点击查看摘要

Abstract:Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains a notable gap in developing interpretable methods for clustering multi-view data. To fill this crucial gap, we make the first attempt towards this direction by introducing an interpretable multi-view clustering framework. Our method begins by extracting embedded features from each view and generates pseudo-labels to guide the initial construction of the decision tree. Subsequently, it iteratively optimizes the feature representation for each view along with refining the interpretable decision tree. Experimental results on real datasets demonstrate that our method not only provides a transparent clustering process for multi-view data but also delivers performance comparable to state-of-the-art multi-view clustering methods. To the best of our knowledge, this is the first effort to design an interpretable clustering framework specifically for multi-view data, opening a new avenue in this field.

ML-97-标题: Machine Learning in Space: Survey ing the Robustness of on-board ML models to Radiation

链接: https://arxiv.org/abs/2405.02642
作者: Kevin Lange, Federico Fontana, Francesco Rossi, Mattia Varile, Giovanni Apruzzese
备注:

点击查看摘要

Abstract:Modern spacecraft are increasingly relying on machine learning (ML). However, physical equipment in space is subject to various natural hazards, such as radiation, which may inhibit the correct operation of computing devices. Despite plenty of evidence showing the damage that naturally-induced faults can cause to ML-related hardware, we observe that the effects of radiation on ML models for space applications are not well-studied. This is a problem: without understanding how ML models are affected by these natural phenomena, it is uncertain “where to start from” to develop radiation-tolerant ML software. As ML researchers, we attempt to tackle this dilemma. By partnering up with space-industry practitioners specialized in ML, we perform a reflective analysis of the state of the art. We provide factual evidence that prior work did not thoroughly examine the impact of natural hazards on ML models meant for spacecraft. Then, through a “negative result”, we show that some existing open-source technologies can hardly be used by researchers to study the effects of radiation for some applications of ML in satellites. As a constructive step forward, we perform simple experiments showcasing how to leverage current frameworks to assess the robustness of practical ML models for cloud detection against radiation-induced faults. Our evaluation reveals that not all faults are as devastating as claimed by some prior work. By publicly releasing our resources, we provide a foothold – usable by researchers without access to spacecraft – for spearheading development of space-tolerant ML models.

ML-98-标题: PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds IJCAI2024

链接: https://arxiv.org/abs/2405.02638
作者: Zehan Zhu, Yan Huang, Xin Wang, Jinming Xu
备注: This paper has been accepted by the 33rd International Joint Conference on Artificial Intelligence(IJCAI 2024)

点击查看摘要

Abstract:In this paper, we propose a differentially private decentralized learning method (termed PrivSGP-VR) which employs stochastic gradient push with variance reduction and guarantees (\epsilon, \delta) -differential privacy (DP) for each node. Our theoretical analysis shows that, under DP Gaussian noise with constant variance, PrivSGP-VR achieves a sub-linear convergence rate of \mathcalO(1/\sqrtnK) , where n and K are the number of nodes and iterations, respectively, which is independent of stochastic gradient variance, and achieves a linear speedup with respect to n . Leveraging the moments accountant method, we further derive an optimal K to maximize the model utility under certain privacy budget in decentralized settings. With this optimized K , PrivSGP-VR achieves a tight utility bound of \mathcalO\left( \sqrtd\log \left( \frac1\delta \right)/(\sqrtnJ\epsilon) \right) , where J and d are the number of local samples and the dimension of decision variable, respectively, which matches that of the server-client distributed counterparts, and exhibits an extra factor of 1/\sqrtn improvement compared to that of the existing decentralized counterparts, such as A(DP) ^2 SGD. Extensive experiments corroborate our theoretical findings, especially in terms of the maximized utility with optimized K , in fully decentralized settings.

ML-99-标题: Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction

链接: https://arxiv.org/abs/2405.02634
作者: Protim Bhattacharjee, Peter Jung
备注:

点击查看摘要

Abstract:The black box nature of deep learning models complicate their usage in critical applications such as remote sensing. Conformal prediction is a method to ensure trust in such scenarios. Subject to data exchangeability, conformal prediction provides finite sample coverage guarantees in the form of a prediction set that is guaranteed to contain the true class within a user defined error rate. In this letter we show that conformal prediction algorithms are related to the uncertainty of the deep learning model and that this relation can be used to detect if the deep learning model is out-of-calibration. Popular classification models like Resnet50, Densenet161, InceptionV3, and MobileNetV2 are applied on remote sensing datasets such as the EuroSAT to demonstrate how under noisy scenarios the model outputs become untrustworthy. Furthermore an out-of-calibration detection procedure relating the model uncertainty and the average size of the conformal prediction set is presented.

ML-100-标题: Unsupervised machine learning for data-driven classification of rock mass using drilling data: How can a data-driven system handle limitations in existing rock mass classification systems?

链接: https://arxiv.org/abs/2405.02631
作者: T. F. Hansen, A. Aarset
备注: 38 pages, 11 figures. Includes ancillary interactive versions of some figures

点击查看摘要

Abstract:Rock mass classification systems are crucial for assessing stability and risk in underground construction globally and guiding support and excavation design. However, systems developed primarily in the 1970s lack access to modern high-resolution data and advanced statistical techniques, limiting their effectiveness as decision-support systems. Initially, we outline the limitations observed in this context and later describe how a data-driven system, based on drilling data as detailed in this study, can overcome these limitations. Using extracted statistical information from thousands of MWD-data values in one-meter sections of a full tunnel profile, thus working as a signature of the rock mass, we have demonstrated that it is possible to form well-defined clusters that can act as a foundational basis for various rock mass classification systems. We reduced the dimensionality of 48-value vectors using nonlinear manifold learning techniques (UMAP) and linear principal component analysis (PCA) to enhance clustering. Unsupervised machine learning methods (HDBSCAN, Agglomerative Clustering, K-means) were employed to cluster the data, with hyperparameters optimised through multi-objective Bayesian optimisation for effective clustering. Using domain knowledge, we experienced improved clustering and system tuning opportunities in adding extra features to core clusters of MWD-data. We structured and correlated these clusters with physical rock mass properties, including labels of rock type and rock quality, and analysed cumulative distributions of key MWD-parameters for rock mass assessment to determine if clusters meaningfully differentiate rock masses. The ability of MWD data to form distinct rock mass clusters suggests substantial potential for future classification systems grounded in this objective, data-driven methodology, free from human bias.

ML-101-标题: Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

链接: https://arxiv.org/abs/2405.02628
作者: Zexing Zhao, Guangsi Shi, Xiaopeng Wu, Ruohua Ren, Xiaojun Gao, Fuyi Li
备注:

点击查看摘要

Abstract:Molecular property prediction is a key component of AI-driven drug discovery and molecular characterization learning. Despite recent advances, existing methods still face challenges such as limited ability to generalize, and inadequate representation of learning from unlabeled data, especially for tasks specific to molecular structures. To address these limitations, we introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. This architecture leverages the power of contrast learning with dual interaction mechanisms and unique molecular graph enhancement strategies. DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization. The framework’s ability to extract key information about molecular structure and higher-order semantics is supported by minimizing loss of contrast. We have established DIG-Mol’s state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks. In addition to demonstrating superior transferability in a small number of learning scenarios, our visualizations highlight DIG-Mol’s enhanced interpretability and representation capabilities. These findings confirm the effectiveness of our approach in overcoming challenges faced by traditional methods and mark a significant advance in molecular property prediction.

ML-102-标题: Learning Linear Utility Functions From Pairwise Comparison Queries ECAI

链接: https://arxiv.org/abs/2405.02612
作者: Luise Ge, Brendan Juba, Yevgeniy Vorobeychik
备注: Submitted to ECAI for review

点击查看摘要

Abstract:We study learnability of linear utility functions from pairwise comparison queries. In particular, we consider two learning objectives. The first objective is to predict out-of-sample responses to pairwise comparisons, whereas the second is to approximately recover the true parameters of the utility function. We show that in the passive learning setting, linear utilities are efficiently learnable with respect to the first objective, both when query responses are uncorrupted by noise, and under Tsybakov noise when the distributions are sufficiently “nice”. In contrast, we show that utility parameters are not learnable for a large set of data distributions without strong modeling assumptions, even when query responses are noise-free. Next, we proceed to analyze the learning problem in an active learning setting. In this case, we show that even the second objective is efficiently learnable, and present algorithms for both the noise-free and noisy query response settings. Our results thus exhibit a qualitative learnability gap between passive and active learning from pairwise preference queries, demonstrating the value of the ability to select pairwise queries for utility learning.

ML-103-标题: Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network

链接: https://arxiv.org/abs/2405.02609
作者: Chen Shao, Elias Giacoumidis, Patrick Matalla, Jialei Li, Shi Li, Sebastian Randel, Andre Richter, Michael Faerber, Tobias Kaefer
备注: 4 pages, 5 figures

点击查看摘要

Abstract:We experimentally demonstrate a novel, low-complexity Fourier Convolution-based Network (FConvNet) based equalizer for 112 Gb/s upstream PAM4-PON. At a BER of 0.005, FConvNet enhances the receiver sensitivity by 2 and 1 dB compared to a 51-tap Sato equalizer and benchmark machine learning algorithms respectively.

ML-104-标题: UDUC: An Uncertainty-driven Approach for Learning-based Robust Control

链接: https://arxiv.org/abs/2405.02598
作者: Yuan Zhang, Jasper Hoffmann, Joschka Boedecker
备注:

点击查看摘要

Abstract:Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasing the ability to capture uncertainty and scalability in high-dimensional control scenarios. However, PE models are susceptible to mode collapse, resulting in non-robust control when faced with environments slightly different from the training set. In this paper, we introduce the \textbfu ncertainty- \textbfd riven rob \textbfu st \textbfc ontrol (UDUC) loss as an alternative objective for training PE models, drawing inspiration from contrastive learning. We analyze the robustness of UDUC loss through the lens of robust optimization and evaluate its performance on the challenging Real-world Reinforcement Learning (RWRL) benchmark, which involves significant environmental mismatches between the training and testing environments.

ML-105-标题: Leveraging (Biased) Information: Multi-armed Bandits with Offline Data ICML2024

链接: https://arxiv.org/abs/2405.02594
作者: Wang Chi Cheung, Lixing Lyu
备注: 24 pages, 5 figures. Accepted to ICML 2024

点击查看摘要

Abstract:We leverage offline data to facilitate online learning in stochastic multi-armed bandits. The probability distributions that govern the offline data and the online rewards can be different. Without any non-trivial upper bound on their difference, we show that no non-anticipatory policy can outperform the UCB policy by (Auer et al. 2002), even in the presence of offline data. In complement, we propose an online policy MIN-UCB, which outperforms UCB when a non-trivial upper bound is given. MIN-UCB adaptively chooses to utilize the offline data when they are deemed informative, and to ignore them otherwise. MIN-UCB is shown to be tight in terms of both instance independent and dependent regret bounds. Finally, we corroborate the theoretical results with numerical experiments.

ML-106-标题: CTD4 - A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics

链接: https://arxiv.org/abs/2405.02576
作者: David Valencia, Henry Williams, Trevor Gee, Bruce A MacDonaland, Minas Liarokapis
备注:

点击查看摘要

Abstract:Categorical Distributional Reinforcement Learning (CDRL) has demonstrated superior sample efficiency in learning complex tasks compared to conventional Reinforcement Learning (RL) approaches. However, the practical application of CDRL is encumbered by challenging projection steps, detailed parameter tuning, and domain knowledge. This paper addresses these challenges by introducing a pioneering Continuous Distributional Model-Free RL algorithm tailored for continuous action spaces. The proposed algorithm simplifies the implementation of distributional RL, adopting an actor-critic architecture wherein the critic outputs a continuous probability distribution. Additionally, we propose an ensemble of multiple critics fused through a Kalman fusion mechanism to mitigate overestimation bias. Through a series of experiments, we validate that our proposed method is easy to train and serves as a sample-efficient solution for executing complex continuous-control tasks.

ML-107-标题: A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System

链接: https://arxiv.org/abs/2405.02574
作者: Sarit Maitra
备注:

点击查看摘要

Abstract:Building operations consume 30% of total power consumption and contribute 26% of global power-related emissions. Therefore, monitoring, and early detection of anomalies at the meter level are essential for residential and commercial buildings. This work investigates both supervised and unsupervised approaches and introduces a dynamic anomaly detection system. The system introduces a supervised Light Gradient Boosting machine and an unsupervised autoencoder with a dynamic threshold. This system is designed to provide real-time detection of anomalies at the meter level. The proposed dynamical system comes with a dynamic threshold based on the Mahalanobis distance and moving averages. This approach allows the system to adapt to changes in the data distribution over time. The effectiveness of the proposed system is evaluated using real-life power consumption data collected from smart metering systems. This empirical testing ensures that the system’s performance is validated under real-world conditions. By detecting unusual data movements and providing early warnings, the proposed system contributes significantly to visual analytics and decision science. Early detection of anomalies enables timely troubleshooting, preventing financial losses and potential disasters such as fire incidents.

ML-108-标题: Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline

链接: https://arxiv.org/abs/2405.02572
作者: Wenjia Meng, Qian Zheng, Long Yang, Yilong Yin, Gang Pan
备注: 12 pages, 3 figures

点击查看摘要

Abstract:Policy-based methods have achieved remarkable success in solving challenging reinforcement learning problems. Among these methods, off-policy policy gradient methods are particularly important due to that they can benefit from off-policy data. However, these methods suffer from the high variance of the off-policy policy gradient (OPPG) estimator, which results in poor sample efficiency during training. In this paper, we propose an off-policy policy gradient method with the optimal action-dependent baseline (Off-OAB) to mitigate this variance issue. Specifically, this baseline maintains the OPPG estimator’s unbiasedness while theoretically minimizing its variance. To enhance practical computational efficiency, we design an approximated version of this optimal baseline. Utilizing this approximation, our method (Off-OAB) aims to decrease the OPPG estimator’s variance during policy optimization. We evaluate the proposed Off-OAB method on six representative tasks from OpenAI Gym and MuJoCo, where it demonstrably surpasses state-of-the-art methods on the majority of these tasks.

ML-109-标题: Decoupling Exploration and Exploitation for Unsupervised Pre-training with Successor Features IJCNN2024

链接: https://arxiv.org/abs/2405.02569
作者: JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain
备注: IJCNN 2024

点击查看摘要

Abstract:Unsupervised pre-training has been on the lookout for the virtue of a value function representation referred to as successor features (SFs), which decouples the dynamics of the environment from the rewards. It has a significant impact on the process of task-specific fine-tuning due to the decomposition. However, existing approaches struggle with local optima due to the unified intrinsic reward of exploration and exploitation without considering the linear regression problem and the discriminator supporting a small skill sapce. We propose a novel unsupervised pre-training model with SFs based on a non-monolithic exploration methodology. Our approach pursues the decomposition of exploitation and exploration of an agent built on SFs, which requires separate agents for the respective purpose. The idea will leverage not only the inherent characteristics of SFs such as a quick adaptation to new tasks but also the exploratory and task-agnostic capabilities. Our suggested model is termed Non-Monolithic unsupervised Pre-training with Successor features (NMPS), which improves the performance of the original monolithic exploration method of pre-training with SFs. NMPS outperforms Active Pre-training with Successor Features (APS) in a comparative experiment.

ML-110-标题: Understanding the Difficulty of Solving Cauchy Problems with PINNs

链接: https://arxiv.org/abs/2405.02561
作者: Tao Wang, Bo Zhao, Sicun Gao, Rose Yu
备注: 13 pages and 18 figures

点击查看摘要

Abstract:Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in recent years. However, they often fail to achieve the same level of accuracy as classical methods in solving differential equations. In this paper, we identify two sources of this issue in the case of Cauchy problems: the use of L^2 residuals as objective functions and the approximation gap of neural networks. We show that minimizing the sum of L^2 residual and initial condition error is not sufficient to guarantee the true solution, as this loss function does not capture the underlying dynamics. Additionally, neural networks are not capable of capturing singularities in the solutions due to the non-compactness of their image sets. This, in turn, influences the existence of global minima and the regularity of the network. We demonstrate that when the global minimum does not exist, machine precision becomes the predominant source of achievable error in practice. We also present numerical experiments in support of our theoretical claims.

ML-111-标题: CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls

链接: https://arxiv.org/abs/2405.02548
作者: Ahmed Bensaoud, Jugal Kalita
备注:

点击查看摘要

Abstract:In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy. This system uses a novel design of combined Convolutional Neural Network and Long Short-Term Memory. We extract opcode sequences and API Calls from Windows malware samples for classification. We transform these features into N-grams (N = 2, 3, and 10)-gram sequences. Our experiments on a dataset of 9,749,57 samples produce high accuracy of 99.91% using the 8-gram sequences. Our method significantly improves the malware classification performance when using a wide range of recent deep learning architectures, leading to state-of-the-art performance. In particular, we experiment with ConvNeXt-T, ConvNeXt-S, RegNetY-4GF, RegNetY-8GF, RegNetY-12GF, EfficientNetV2, Sequencer2D-L, Swin-T, ViT-G/14, ViT-Ti, ViT-S, VIT-B, VIT-L, and MaxViT-B. Among these architectures, Swin-T and Sequencer2D-L architectures achieved high accuracies of 99.82% and 99.70%, respectively, comparable to our CNN-LSTM architecture although not surpassing it.

ML-112-标题: A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Dataset s

链接: https://arxiv.org/abs/2405.02534
作者: Karim Salta, Tomojit Ghosh, Michael Kirby
备注:

点击查看摘要

Abstract:In this paper a multi-domain multi-task algorithm for feature selection in bulk RNAseq data is proposed. Two datasets are investigated arising from mouse host immune response to Salmonella infection. Data is collected from several strains of collaborative cross mice. Samples from the spleen and liver serve as the two domains. Several machine learning experiments are conducted and the small subset of discriminative across domains features have been extracted in each case. The algorithm proves viable and underlines the benefits of across domain feature selection by extracting new subset of discriminative features which couldn’t be extracted only by one-domain approach.

ML-113-标题: A Survey of Few-Shot Learning for Biomedical Time Series

链接: https://arxiv.org/abs/2405.02485
作者: Chenqi Li, Timothy Denison, Tingting Zhu
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

点击查看摘要

Abstract:Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitating early disease detection and intervention, as well as promoting personalized healthcare delivery. However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to leverage past experiences to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The clinical benefits and limitations of such methods are discussed in relation to traditional data-driven approaches. This paper aims to provide insights into the current landscape of few-shot learning for biomedical time series and its implications for future research and applications.

ML-114-标题: Proximal Curriculum with Task Correlations for Deep Reinforcement Learning IJCAI’24

链接: https://arxiv.org/abs/2405.02481
作者: Georgios Tzannetos, Parameswaran Kamalaruban, Adish Singla
备注: IJCAI’24 paper (longer version)

点击查看摘要

Abstract:Curriculum design for reinforcement learning (RL) can speed up an agent’s learning process and help it learn to perform well on complex tasks. However, existing techniques typically require domain-specific hyperparameter tuning, involve expensive optimization procedures for task selection, or are suitable only for specific learning objectives. In this work, we consider curriculum design in contextual multi-task settings where the agent’s final performance is measured w.r.t. a target distribution over complex tasks. We base our curriculum design on the Zone of Proximal Development concept, which has proven to be effective in accelerating the learning process of RL agents for uniform distribution over all tasks. We propose a novel curriculum, ProCuRL-Target, that effectively balances the need for selecting tasks that are not too difficult for the agent while progressing the agent’s learning toward the target distribution via leveraging task correlations. We theoretically justify the task selection strategy of ProCuRL-Target by analyzing a simple learning setting with REINFORCE learner model. Our experimental results across various domains with challenging target task distributions affirm the effectiveness of our curriculum strategy over state-of-the-art baselines in accelerating the training process of deep RL agents.

ML-115-标题: Continuous Learned Primal Dual

链接: https://arxiv.org/abs/2405.02478
作者: Christina Runkel, Ander Biguri, Carola-Bibiane Schönlieb
备注:

点击查看摘要

Abstract:Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be directly modelled by a parameterised ODE. This idea has had resounding success in the deep learning literature, with direct or indirect influence in many state of the art ideas, such as diffusion models or time dependant models. Recently, a continuous version of the U-net architecture has been proposed, showing increased performance over its discrete counterpart in many imaging applications and wrapped with theoretical guarantees around its performance and robustness. In this work, we explore the use of Neural ODEs for learned inverse problems, in particular with the well-known Learned Primal Dual algorithm, and apply it to computed tomography (CT) reconstruction.

ML-116-标题: Generalizing Orthogonalization for Models with Non-linearities

链接: https://arxiv.org/abs/2405.02475
作者: David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
备注:

点击查看摘要

Abstract:The complexity of black-box algorithms can lead to various challenges, including the introduction of biases. These biases present immediate risks in the algorithms’ application. It was, for instance, shown that neural networks can deduce racial information solely from a patient’s X-ray scan, a task beyond the capability of medical experts. If this fact is not known to the medical expert, automatic decision-making based on this algorithm could lead to prescribing a treatment (purely) based on racial information. While current methodologies allow for the “orthogonalization” or “normalization” of neural networks with respect to such information, existing approaches are grounded in linear models. Our paper advances the discourse by introducing corrections for non-linearities such as ReLU activations. Our approach also encompasses scalar and tensor-valued predictions, facilitating its integration into neural network architectures. Through extensive experiments, we validate our method’s effectiveness in safeguarding sensitive data in generalized linear models, normalizing convolutional neural networks for metadata, and rectifying pre-existing embeddings for undesired attributes.

ML-117-标题: ProFLingo: A Fingerprinting-based Copyright Protection Scheme for Large Language Models

链接: https://arxiv.org/abs/2405.02466
作者: Heng Jin, Chaoyu Zhang, Shanghao Shi, Wenjing Lou, Y. Thomas Hou
备注: This is the author’s pre-print version of the work. It is posted here for your personal use. Not for redistribution

点击查看摘要

Abstract:Large language models (LLMs) have attracted significant attention in recent years. Due to their “Large” nature, training LLMs from scratch consumes immense computational resources. Since several major players in the artificial intelligence (AI) field have open-sourced their original LLMs, an increasing number of individual researchers and smaller companies are able to build derivative LLMs based on these open-sourced models at much lower costs. However, this practice opens up possibilities for unauthorized use or reproduction that may not comply with licensing agreements, and deriving models can change the model’s behavior, thus complicating the determination of model ownership. Current copyright protection schemes for LLMs are either designed for white-box settings or require additional modifications to the original model, which restricts their use in real-world settings. In this paper, we propose ProFLingo, a black-box fingerprinting-based copyright protection scheme for LLMs. ProFLingo generates adversarial examples (AEs) that can represent the unique decision boundary characteristics of an original model, thereby establishing unique fingerprints. Our scheme checks the effectiveness of these adversarial examples on a suspect model to determine whether it has been derived from the original model. ProFLingo offers a non-invasive approach, which neither requires knowledge of the suspect model nor modifications to the base model or its training process. To the best of our knowledge, our method represents the first black-box fingerprinting technique for copyright protection for LLMs. Our source code and generated AEs are available at: this https URL.

ML-118-标题: Learning minimal volume uncertainty ellipsoids

链接: https://arxiv.org/abs/2405.02441
作者: Itai Alon, David Arnon, Ami Wiesel
备注:

点击查看摘要

Abstract:We consider the problem of learning uncertainty regions for parameter estimation problems. The regions are ellipsoids that minimize the average volumes subject to a prescribed coverage probability. As expected, under the assumption of jointly Gaussian data, we prove that the optimal ellipsoid is centered around the conditional mean and shaped as the conditional covariance matrix. In more practical cases, we propose a differentiable optimization approach for approximately computing the optimal ellipsoids using a neural network with proper calibration. Compared to existing methods, our network requires less storage and less computations in inference time, leading to accurate yet smaller ellipsoids. We demonstrate these advantages on four real-world localization datasets.

ML-119-标题: FastLloyd: Federated Accurate Secure and Tunable k-Means Clustering with Differential Privacy

链接: https://arxiv.org/abs/2405.02437
作者: Abdulrahman Diaa, Thomas Humphries, Florian Kerschbaum
备注:

点击查看摘要

Abstract:We study the problem of privacy-preserving k -means clustering in the horizontally federated setting. Existing federated approaches using secure computation, suffer from substantial overheads and do not offer output privacy. At the same time, differentially private (DP) k -means algorithms assume a trusted central curator and do not extend to federated settings. Naively combining the secure and DP solutions results in a protocol with impractical overhead. Instead, our work provides enhancements to both the DP and secure computation components, resulting in a design that is faster, more private, and more accurate than previous work. By utilizing the computational DP model, we design a lightweight, secure aggregation-based approach that achieves four orders of magnitude speed-up over state-of-the-art related work. Furthermore, we not only maintain the utility of the state-of-the-art in the central model of DP, but we improve the utility further by taking advantage of constrained clustering techniques.

ML-120-标题: A Unified Framework for Human-Allied Learning of Probabilistic Circuits

链接: https://arxiv.org/abs/2405.02413
作者: Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
备注:

点击查看摘要

Abstract:Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on PCs predominantly concentrates on data-driven parameter learning, often neglecting the potential of knowledge-intensive learning, a particular issue in data-scarce/knowledge-rich domains such as healthcare. To bridge this gap, we propose a novel unified framework that can systematically integrate diverse domain knowledge into the parameter learning process of PCs. Experiments on several benchmarks as well as real world datasets show that our proposed framework can both effectively and efficiently leverage domain knowledge to achieve superior performance compared to purely data-driven learning approaches.

ML-121-标题: Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting

链接: https://arxiv.org/abs/2405.02412
作者: Daniel Frees, Pranav Ravella, Charlie Zhang
备注: 10 pages

点击查看摘要

Abstract:This paper presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs). We evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming player FPL score based on historical FPL data over the previous weeks. Our baseline models, Ridge regression and LightGBM, achieve solid performance and emphasize the importance of recent FPL points, influence, creativity, threat, and playtime in predicting EPL player performances. Our optimal CNN architecture achieves better performance with fewer input features and even outperforms the best previous EPL player performance forecasting models in the literature. The optimal CNN architecture also achieves very strong Spearman correlation with player rankings, indicating its strong implications for supporting the development of FPL artificial intelligence (AI) Agents and providing analysis for FPL managers. We additionally perform transfer learning experiments on soccer news data collected from The Guardian, for the same task of predicting upcoming player score, but do not identify a strong predictive signal in natural language news texts, achieving worse performance compared to both the CNN and baseline models. Overall, our CNN-based approach marks a significant advancement in EPL player performance forecasting and lays the foundation for transfer learning to other EPL prediction tasks such as win-loss odds for sports betting and the development of cutting-edge FPL AI Agents.

ML-122-标题: Efficient Deep Learning with Decorrelated Backpropagation

链接: https://arxiv.org/abs/2405.02385
作者: Sander Dalm, Joshua Offergeld, Nasir Ahmad, Marcel van Gerven
备注:

点击查看摘要

Abstract:The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a significant computational cost and therefore a high carbon footprint. Converging evidence suggests that input decorrelation may speed up deep learning. However, to date, this has not yet translated into substantial improvements in training efficiency in large-scale DNNs. This is mainly caused by the challenge of enforcing fast and stable network-wide decorrelation. Here, we show for the first time that much more efficient training of very deep neural networks using decorrelated backpropagation is feasible. To achieve this goal we made use of a novel algorithm which induces network-wide input decorrelation using minimal computational overhead. By combining this algorithm with careful optimizations, we obtain a more than two-fold speed-up and higher test accuracy compared to backpropagation when training a 18-layer deep residual network. This demonstrates that decorrelation provides exciting prospects for efficient deep learning at scale.

ML-123-标题: CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding

链接: https://arxiv.org/abs/2405.02384
作者: Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long
备注:

点击查看摘要

Abstract:Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of the visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theory, emulating its two core mechanisms: Correcting predictions from residuals and hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks and ignore the Precision Weighting mechanism of PC theory. The precision weighting mechanism posits that the brain allocates more attention to signals with lower precision, contributing to the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM), which demonstrate the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models and weight the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights effectively estimate the data predictability. We apply CogDPM to real-world prediction tasks using the United Kindom precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models by providing more proficient forecasting.

ML-124-标题: Robustness of Decentralised Learning to Nodes and Data Disruption

链接: https://arxiv.org/abs/2405.02377
作者: Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti, János Kertész
备注: Supported by the H2020 HumaneAI Net (952026), CHIST-ERA-19-XAI010 SAI, PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 FAIR, PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000001 RESTART

点击查看摘要

Abstract:In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated and to share knowledge extracted from local data among themselves through an interactive process of collaborative refinement. This paradigm supports scenarios where data cannot leave local nodes due to privacy or sovereignty reasons or real-time constraints imposing proximity of models to locations where inference has to be carried out. The distributed nature of decentralised learning implies significant new research challenges with respect to centralised learning. Among them, in this paper, we focus on robustness issues. Specifically, we study the effect of nodes’ disruption on the collective learning process. Assuming a given percentage of “central” nodes disappear from the network, we focus on different cases, characterised by (i) different distributions of data across nodes and (ii) different times when disruption occurs with respect to the start of the collaborative learning task. Through these configurations, we are able to show the non-trivial interplay between the properties of the network connecting nodes, the persistence of knowledge acquired collectively before disruption or lack thereof, and the effect of data availability pre- and post-disruption. Our results show that decentralised learning processes are remarkably robust to network disruption. As long as even minimum amounts of data remain available somewhere in the network, the learning process is able to recover from disruptions and achieve significant classification accuracy. This clearly varies depending on the remaining connectivity after disruption, but we show that even nodes that remain completely isolated can retain significant knowledge acquired before the disruption.

ML-125-标题: The Sparse Tsetlin Machine: Sparse Representation with Active Literals

链接: https://arxiv.org/abs/2405.02375
作者: Sebastian Østby, Tobias M. Brambo, Sondre Glimsdal
备注:

点击查看摘要

Abstract:This paper introduces the Sparse Tsetlin Machine (STM), a novel Tsetlin Machine ™ that processes sparse data efficiently. Traditionally, the TM does not consider data characteristics such as sparsity, commonly seen in NLP applications and other bag-of-word-based representations. Consequently, a TM must initialize, store, and process a significant number of zero values, resulting in excessive memory usage and computational time. Previous attempts at creating a sparse TM have predominantly been unsuccessful, primarily due to their inability to identify which literals are sufficient for TM training. By introducing Active Literals (AL), the STM can focus exclusively on literals that actively contribute to the current data representation, significantly decreasing memory footprint and computational time while demonstrating competitive classification performance.

ML-126-标题: Architecture of a Cortex Inspired Hierarchical Event Recaller

链接: https://arxiv.org/abs/2405.02371
作者: Valentin Puente Varona
备注:

点击查看摘要

Abstract:This paper proposes a new approach to Machine Learning (ML) that focuses on unsupervised continuous context-dependent learning of complex patterns. Although the proposal is partly inspired by some of the current knowledge about the structural and functional properties of the mammalian brain, we do not claim that biological systems work in an analogous way (nor the opposite). Based on some properties of the cerebellar cortex and adjacent structures, a proposal suitable for practical problems is presented. A synthetic structure capable of identifying and predicting complex temporal series will be defined and experimentally tested. The system relies heavily on prediction to help identify and learn patterns based on previously acquired contextual knowledge. As a proof of concept, the proposed system is shown to be able to learn, identify and predict a remarkably complex temporal series such as human speech, with no prior knowledge. From raw data, without any adaptation in the core algorithm, the system is able to identify certain speech structures from a set of Spanish sentences. Unlike conventional ML, the proposal can learn with a reduced training set. Although the idea can be applied to a constrained problem, such as the detection of unknown vocabulary in a speech, it could be used in more applications, such as vision, or (by incorporating the missing biological periphery) fit into other ML techniques. Given the trivial computational primitives used, a potential hardware implementation will be remarkably frugal. Coincidentally, the proposed model not only conforms to a plausible functional framework for biological systems but may also explain many elusive cognitive phenomena.

ML-127-标题: No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks

链接: https://arxiv.org/abs/2405.02369
作者: Feng-Lei Fan, Meng Wang, Hang-Cheng Dong, Jianwei Ma, Tieyong Zeng
备注: 12 pages, 4 figures

点击查看摘要

Abstract:Biologically, the brain does not rely on a single type of neuron that universally functions in all aspects. Instead, it acts as a sophisticated designer of task-based neurons. In this study, we address the following question: since the human brain is a task-based neuron user, can the artificial network design go from the task-based architecture design to the task-based neuron design? Since methodologically there are no one-size-fits-all neurons, given the same structure, task-based neurons can enhance the feature representation ability relative to the existing universal neurons due to the intrinsic inductive bias for the task. Specifically, we propose a two-step framework for prototyping task-based neurons. First, symbolic regression is used to identify optimal formulas that fit input data by utilizing base functions such as logarithmic, trigonometric, and exponential functions. We introduce vectorized symbolic regression that stacks all variables in a vector and regularizes each input variable to perform the same computation, which can expedite the regression speed, facilitate parallel computation, and avoid overfitting. Second, we parameterize the acquired elementary formula to make parameters learnable, which serves as the aggregation function of the neuron. The activation functions such as ReLU and the sigmoidal functions remain the same because they have proven to be good. Empirically, experimental results on synthetic data, classic benchmarks, and real-world applications show that the proposed task-based neuron design is not only feasible but also delivers competitive performance over other state-of-the-art models.

ML-128-标题: A Survey on Contribution Evaluation in Vertical Federated Learning

链接: https://arxiv.org/abs/2405.02364
作者: Yue Cui, Chung-ju Huang, Yuzhu Zhang, Leye Wang, Lixin Fan, Xiaofang Zhou, Qiang Yang
备注:

点击查看摘要

Abstract:Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL facilitates collaboration among multiple entities with distinct feature sets on the same user population, enabling the joint training of predictive models without direct data sharing. A key aspect of VFL is the fair and accurate evaluation of each entity’s contribution to the learning process. This is crucial for maintaining trust among participating entities, ensuring equitable resource sharing, and fostering a sustainable collaboration framework. This paper provides a thorough review of contribution evaluation in VFL. We categorize the vast array of contribution evaluation techniques along the VFL lifecycle, granularity of evaluation, privacy considerations, and core computational methods. We also explore various tasks in VFL that involving contribution evaluation and analyze their required evaluation properties and relation to the VFL lifecycle phases. Finally, we present a vision for the future challenges of contribution evaluation in VFL. By providing a structured analysis of the current landscape and potential advancements, this paper aims to guide researchers and practitioners in the design and implementation of more effective, efficient, and privacy-centric VFL solutions. Relevant literature and open-source resources have been compiled and are being continuously updated at the GitHub repository: \urlthis https URL.

ML-129-标题: Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning

链接: https://arxiv.org/abs/2405.02360
作者: Yanli Li, Jehad Ibrahim, Huaming Chen, Dong Yuan, Kim-Kwang Raymond Choo
备注:

点击查看摘要

Abstract:A large number of federated learning (FL) algorithms have been proposed for different applications and from varying perspectives. However, the evaluation of such approaches often relies on a single metric (e.g., accuracy). Such a practice fails to account for the unique demands and diverse requirements of different use cases. Thus, how to comprehensively evaluate an FL algorithm and determine the most suitable candidate for a designated use case remains an open question. To mitigate this research gap, we introduce the Holistic Evaluation Metrics (HEM) for FL in this work. Specifically, we collectively focus on three primary use cases, which are Internet of Things (IoT), smart devices, and institutions. The evaluation metric encompasses various aspects including accuracy, convergence, computational efficiency, fairness, and personalization. We then assign a respective importance vector for each use case, reflecting their distinct performance requirements and priorities. The HEM index is finally generated by integrating these metric components with their respective importance vectors. Through evaluating different FL algorithms in these three prevalent use cases, our experimental results demonstrate that HEM can effectively assess and identify the FL algorithms best suited to particular scenarios. We anticipate this work sheds light on the evaluation process for pragmatic FL algorithms in real-world applications.

ML-130-标题: CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection

链接: https://arxiv.org/abs/2405.02359
作者: Jindong Li, Qianli Xing, Qi Wang, Yi Chang
备注:

点击查看摘要

Abstract:Unsupervised graph-level anomaly detection (UGAD) has received remarkable performance in various critical disciplines, such as chemistry analysis and bioinformatics. Existing UGAD paradigms often adopt data augmentation techniques to construct multiple views, and then employ different strategies to obtain representations from different views for jointly conducting UGAD. However, most previous works only considered the relationship between nodes/graphs from a limited receptive field, resulting in some key structure patterns and feature information being neglected. In addition, most existing methods consider different views separately in a parallel manner, which is not able to explore the inter-relationship across different views directly. Thus, a method with a larger receptive field that can explore the inter-relationship across different views directly is in need. In this paper, we propose a novel Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection, namely, CVTGAD. To increase the receptive field, we construct a simplified transformer-based module, exploiting the relationship between nodes/graphs from both intra-graph and inter-graph perspectives. Furthermore, we design a cross-view attention mechanism to directly exploit the view co-occurrence between different views, bridging the inter-view gap at node level and graph level. To the best of our knowledge, this is the first work to apply transformer and cross attention to UGAD, which realizes graph neural network and transformer working collaboratively. Extensive experiments on 15 real-world datasets of 3 fields demonstrate the superiority of CVTGAD on the UGAD task. The code is available at \urlthis https URL.

ML-131-标题: A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Mode

链接: https://arxiv.org/abs/2405.02358
作者: Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, Fugee Tsung
备注: 5 figures, 6 tables, 41 pages

点击查看摘要

Abstract:Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language foundation models have unveiled their remarkable capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. This success has sparked interest in the exploration of foundation models to solve multiple time series challenges simultaneously. There are two main research lines, namely \textbfpre-training foundation models from scratch for time series and \textbfadapting large language foundation models for time series. They both contribute to the development of a unified model that is highly generalizable, versatile, and comprehensible for time series analysis. This survey offers a 3E analytical framework for comprehensive examination of related research. Specifically, we examine existing works from three dimensions, namely \textbfEffectiveness, \textbfEfficiency and \textbfExplainability. In each dimension, we focus on discussing how related works devise tailored solution by considering unique challenges in the realm of time series.Furthermore, we provide a domain taxonomy to help followers keep up with the domain-specific advancements. In addition, we introduce extensive resources to facilitate the field’s development, including datasets, open-source, time series libraries. A GitHub repository is also maintained for resource updates (this https URL).

ML-132-标题: Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks

链接: https://arxiv.org/abs/2405.02357
作者: Zijian Zhang, Yujie Sun, Zepu Wang, Yuqi Nie, Xiaobo Ma, Peng Sun, Ruolin Li
备注: 9 pages

点击查看摘要

Abstract:Mobility analysis is a crucial element in the research area of transportation systems. Forecasting traffic information offers a viable solution to address the conflict between increasing transportation demands and the limitations of transportation infrastructure. Predicting human travel is significant in aiding various transportation and urban management tasks, such as taxi dispatch and urban planning. Machine learning and deep learning methods are favored for their flexibility and accuracy. Nowadays, with the advent of large language models (LLMs), many researchers have combined these models with previous techniques or applied LLMs to directly predict future traffic information and human travel behaviors. However, there is a lack of comprehensive studies on how LLMs can contribute to this field. This survey explores existing approaches using LLMs for mobility forecasting problems. We provide a literature review concerning the forecasting applications within transportation systems, elucidating how researchers utilize LLMs, showcasing recent state-of-the-art advancements, and identifying the challenges that must be overcome to fully leverage LLMs in this domain.

ML-133-标题: Stochastic Multivariate Universal-Radix Finite-State Machine: a Theoretically and Practically Elegant Nonlinear Function Approximator

链接: https://arxiv.org/abs/2405.02356
作者: Xincheng Feng, Guodong Shen, Jianhao Hu, Meng Li, Ngai Wong
备注:

点击查看摘要

Abstract:Nonlinearities are crucial for capturing complex input-output relationships especially in deep neural networks. However, nonlinear functions often incur various hardware and compute overheads. Meanwhile, stochastic computing (SC) has emerged as a promising approach to tackle this challenge by trading output precision for hardware simplicity. To this end, this paper proposes a first-of-its-kind stochastic multivariate universal-radix finite-state machine (SMURF) that harnesses SC for hardware-simplistic multivariate nonlinear function generation at high accuracy. We present the finite-state machine (FSM) architecture for SMURF, as well as analytical derivations of sampling gate coefficients for accurately approximating generic nonlinear functions. Experiments demonstrate the superiority of SMURF, requiring only 16.07% area and 14.45% power consumption of Taylor-series approximation, and merely 2.22% area of look-up table (LUT) schemes.

ML-134-标题: Heterogeneous network and graph attention auto-encoder for LncRNA-disease association prediction

链接: https://arxiv.org/abs/2405.02354
作者: Jin-Xing Liu, Wen-Yu Xi, Ling-Yun Dai, Chun-Hou Zheng, Ying-Lian Gao
备注: 10 pages, 8 figures

点击查看摘要

Abstract:The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs.

ML-135-标题: Towards General Neural Surrogate Solvers with Specialized Neural Accelerators ICML2024

链接: https://arxiv.org/abs/2405.02351
作者: Chenkai Mao, Robert Lupoiu, Tianxiang Dai, Mingkun Chen, Jonathan A. Fan
备注: 8 pages, 7 Figures, to be published in ICML 2024

点击查看摘要

Abstract:Surrogate neural network-based partial differential equation (PDE) solvers have the potential to solve PDEs in an accelerated manner, but they are largely limited to systems featuring fixed domain sizes, geometric layouts, and boundary conditions. We propose Specialized Neural Accelerator-Powered Domain Decomposition Methods (SNAP-DDM), a DDM-based approach to PDE solving in which subdomain problems containing arbitrary boundary conditions and geometric parameters are accurately solved using an ensemble of specialized neural operators. We tailor SNAP-DDM to 2D electromagnetics and fluidic flow problems and show how innovations in network architecture and loss function engineering can produce specialized surrogate subdomain solvers with near unity accuracy. We utilize these solvers with standard DDM algorithms to accurately solve freeform electromagnetics and fluids problems featuring a wide range of domain sizes.

ML-136-标题: What makes Models Compositional? A Theoretical View: With Supplement IJCAI2024

链接: https://arxiv.org/abs/2405.02350
作者: Parikshit Ram, Tim Klinger, Alexander G. Gray
备注: Extended version of the original IJCAI 2024 paper with detailed supplementary materials (27 pages, 7 figures)

点击查看摘要

Abstract:Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of existing sequence processing models. These benchmarks often highlight failures of existing models, but it is not clear why these models fail in this way. In this paper, we seek to theoretically understand the role the compositional structure of the models plays in these failures and how this structure relates to their expressivity and sample complexity. We propose a general neuro-symbolic definition of compositional functions and their compositional complexity. We then show how various existing general and special purpose sequence processing models (such as recurrent, convolution and attention-based ones) fit this definition and use it to analyze their compositional complexity. Finally, we provide theoretical guarantees for the expressivity and systematic generalization of compositional models that explicitly depend on our proposed definition and highlighting factors which drive poor empirical performance.

ML-137-标题: Explainable Muti-Label Classification of MBTI Types

链接: https://arxiv.org/abs/2405.02349
作者: Siana Kong, Marina Sokolova
备注: 22 pages, 12 tables, 2 figure

点击查看摘要

Abstract:In this study, we aim to identify the most effective machine learning model for accurately classifying Myers-Briggs Type Indicator (MBTI) types from Reddit posts and a Kaggle data set. We apply multi-label classification using the Binary Relevance method. We use Explainable Artificial Intelligence (XAI) approach to highlight the transparency and understandability of the process and result. To achieve this, we experiment with glass-box learning models, i.e. models designed for simplicity, transparency, and interpretability. We selected k-Nearest Neighbour, Multinomial Naive Bayes, and Logistic Regression for the glass-box models. We show that Multinomial Naive Bayes and k-Nearest Neighbour perform better if classes with Observer (S) traits are excluded, whereas Logistic Regression obtains its best results when all classes have > 550 entries.

ML-138-标题: Temporal assessment of malicious behaviors: application to turnout field data monitoring

链接: https://arxiv.org/abs/2405.02346
作者: Sara Abdellaoui, Emil Dumitrescu, Cédric Escudero, Eric Zamaï
备注: To be published in the International Conference on Control, Automation and Diagnosis (ICCAD24)

点击查看摘要

Abstract:Monitored data collected from railway turnouts are vulnerable to cyberattacks: attackers may either conceal failures or trigger unnecessary maintenance actions. To address this issue, a cyberattack investigation method is proposed based on predictions made from the temporal evolution of the turnout behavior. These predictions are then compared to the field acquired data to detect any discrepancy. This method is illustrated on a collection of real-life data.

ML-139-标题: Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attribution Methods

链接: https://arxiv.org/abs/2405.02344
作者: Peiyu Yang, Naveed Akhtar, Jiantong Jiang, Ajmal Mian
备注:

点击查看摘要

Abstract:Attribution methods compute importance scores for input features to explain the output predictions of deep models. However, accurate assessment of attribution methods is challenged by the lack of benchmark fidelity for attributing model predictions. Moreover, other confounding factors in attribution estimation, including the setup choices of post-processing techniques and explained model predictions, further compromise the reliability of the evaluation. In this work, we first identify a set of fidelity criteria that reliable benchmarks for attribution methods are expected to fulfill, thereby facilitating a systematic assessment of attribution benchmarks. Next, we introduce a Backdoor-based eXplainable AI benchmark (BackX) that adheres to the desired fidelity criteria. We theoretically establish the superiority of our approach over the existing benchmarks for well-founded attribution evaluation. With extensive analysis, we also identify a setup for a consistent and fair benchmarking of attribution methods across different underlying methodologies. This setup is ultimately employed for a comprehensive comparison of existing methods using our BackX benchmark. Finally, our analysis also provides guidance for defending against backdoor attacks with the help of attribution methods.

ML-140-标题: Improved Communication-Privacy Trade-offs in L_2 Mean Estimation under Streaming Differential Privacy

链接: https://arxiv.org/abs/2405.02341
作者: Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
备注:

点击查看摘要

Abstract:We study L_2 mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for L_\infty geometry and rely on random rotation or Kashin’s representation to adapt to L_2 geometry, resulting in suboptimal leading constants in mean square errors (MSEs); secondly, schemes achieving order-optimal communication-privacy trade-offs do not extend seamlessly to streaming differential privacy (DP) settings (e.g., tree aggregation or matrix factorization), rendering them incompatible with DP-FTRL type optimizers. In this work, we tackle these issues by introducing a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP noise. Unlike previous approaches, our accounting algorithm directly operates in L_2 geometry, yielding MSEs that fast converge to those of the uncompressed Gaussian mechanism. Additionally, we extend the sparsification scheme to the matrix factorization framework under streaming DP and provide a precise accountant tailored for DP-FTRL type optimizers. Empirically, our method demonstrates at least a 100x improvement of compression for DP-SGD across various FL tasks.

ML-141-标题: Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G

链接: https://arxiv.org/abs/2405.02336
作者: Walid Saad, Omar Hashash, Christo Kurisummoottil Thomas, Christina Chaccour, Merouane Debbah, Narayan Mandayam, Zhu Han
备注:

点击查看摘要

Abstract:Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. Such tools struggle to cope with the non-trivial challenges of the network environment and the growing demands of emerging use cases. In this paper, we revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. These systems acquire common sense by exploiting different cognitive abilities such as perception, analogy, and reasoning, that enable them to generalize and deal with unforeseen scenarios. Towards developing the components of such a system, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyper-dimensional (HD) computing, that aligns with intuitive physics and enables analogical reasoning, that define common sense. Then, we explain how methods such as integrated information theory play a role in the proposed intent-driven and objective-driven planning methods that maneuver the AGI-native network to take actions. Next, we discuss how an AGI-native network can enable use cases related to human and autonomous agents: a) analogical reasoning for next-generation DTs, b) synchronized and resilient experiences for cognitive avatars, and c) brain-level metaverse experiences like holographic teleportation. Finally, we conclude with a set of recommendations to build AGI-native systems. Ultimately, we envision this paper as a roadmap for the beyond 6G era.

ML-142-标题: sDAC – Semantic Digital Analog Converter for Semantic Communications

链接: https://arxiv.org/abs/2405.02335
作者: Zhicheng Bao, Chen Dong, Xiaodong Xu
备注:

点击查看摘要

Abstract:In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility of semantic communications and digital communications. Most of the current semantic communication systems are based on the analog modulations, ignoring their incorporation with digital communication systems, which are more common in practice. In fact, quantization methods in traditional communication systems are not appropriate for use in the era of semantic communication as these methods do not consider the semantic information inside symbols. In this case, any bit flip caused by channel noise can lead to a great performance drop. To address this challenge, sDAC is proposed. It is a simple yet efficient and generative module used to realize digital and analog bi-directional conversion. On the transmitter side, continuous values from the encoder are converted to binary bits and then can be modulated by any existing methods. After transmitting through the noisy channel, these bits get demodulated by paired methods and converted back to continuous values for further semantic decoding. The whole progress does not depend on any specific semantic model, modulation methods, or channel conditions. In the experiment section, the performance of sDAC is tested across different semantic models, semantic tasks, modulation methods, channel conditions and quantization orders. Test results show that the proposed sDAC has great generative properties and channel robustness.

ML-143-标题: Adaptive Semantic Token Selection for AI-native Goal-oriented Communications

链接: https://arxiv.org/abs/2405.02330
作者: Alessio Devoto, Simone Petruzzi, Jary Pomponi, Paolo Di Lorenzo, Simone Scardapane
备注: 5 pages

点击查看摘要

Abstract:In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). Here, we consider a dynamic model where communication happens over a channel with variable latency and bandwidth constraints. Leveraging recent works on conditional computation, we exploit the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism that learns to select relevant tokens (e.g., image patches) from the input signal. This is done dynamically, on a per-input basis, with a rate that can be chosen as an additional input by the user. We show that our model improves over state-of-the-art token selection mechanisms, exhibiting high accuracy for a wide range of latency and bandwidth constraints, without the need for deploying multiple architectures tailored to each constraint. Last, but not least, the proposed token selection mechanism helps extract powerful semantics that are easy to understand and explain, paving the way for interpretable-by-design models for the next generation of AI-native communication systems.

ML-144-标题: CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture

链接: https://arxiv.org/abs/2405.02323
作者: Jonas Ney, Christoph Füllner, Vincent Lauinger, Laurent Schmalen, Sebastian Randel, Norbert Wehn
备注: The article was submitted to the International Journal of Parallel Programming (IJPP) and is currently under review

点击查看摘要

Abstract:To satisfy the growing throughput demand of data-intensive applications, the performance of optical communication systems increased dramatically in recent years. With higher throughput, more advanced equalizers are crucial, to compensate for impairments caused by inter-symbol interference (ISI). The latest research shows that artificial neural network (ANN)-based equalizers are promising candidates to replace traditional algorithms for high-throughput communications. On the other hand, not only throughput but also flexibility is a main objective of beyond-5G and 6G communication systems. A platform that is able to satisfy the strict throughput and flexibility requirements of modern communication systems are field programmable gate arrays (FPGAs). Thus, in this work, we present a high-performance FPGA implementation of an ANN-based equalizer, which meets the throughput requirements of modern optical communication systems. Further, our architecture is highly flexible since it includes a variable degree of parallelism (DOP) and therefore can also be applied to low-cost or low-power applications which is demonstrated for a magnetic recording channel. The implementation is based on a cross-layer design approach featuring optimizations from the algorithm down to the hardware architecture, including a detailed quantization analysis. Moreover, we present a framework to reduce the latency of the ANN-based equalizer under given throughput constraints. As a result, the bit error ratio (BER) of our equalizer for the optical fiber channel is around four times lower than that of a conventional one, while the corresponding FPGA implementation achieves a throughput of more than 40 GBd, outperforming a high-performance graphics processing unit (GPU) by three orders of magnitude for a similar batch size.

ML-145-标题: A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning Spiking Neural Networks and Local Plasticity Rules

链接: https://arxiv.org/abs/2405.02316
作者: Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
备注: 13 pages, 19 figures

点击查看摘要

Abstract:This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalability, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 111 nJ (0.3% of conventional computing requirements). The results demonstrate the system’s adjustment to changing work environments and its efficient use of computational and energy resources, with a moderate increase in energy consumption of 27.2% and 37% for static and dynamic obstacles, respectively, compared to non-obstacle scenarios.

ML-146-标题: Deep Reinforcement Learning for Modelling Protein Complexes ICLR2024

链接: https://arxiv.org/abs/2405.02299
作者: Tao Feng, Ziqi Gao, Jiaxuan You, Chenyi Zi, Yan Zhou, Chen Zhang, Jia Li
备注: International Conference on Learning Representations (ICLR 2024)

点击查看摘要

Abstract:AlphaFold can be used for both single-chain and multi-chain protein structure prediction, while the latter becomes extremely challenging as the number of chains increases. In this work, by taking each chain as a node and assembly actions as edges, we show that an acyclic undirected connected graph can be used to predict the structure of multi-chain protein complexes (a.k.a., protein complex modelling, PCM). However, there are still two challenges: 1) The huge combinatorial optimization space of N^N-2 ( N is the number of chains) for the PCM problem can easily lead to high computational cost. 2) The scales of protein complexes exhibit distribution shift due to variance in chain numbers, which calls for the generalization in modelling complexes of various scales. To address these challenges, we propose GAPN, a Generative Adversarial Policy Network powered by domain-specific rewards and adversarial loss through policy gradient for automatic PCM prediction. Specifically, GAPN learns to efficiently search through the immense assembly space and optimize the direct docking reward through policy gradient. Importantly, we design an adversarial reward function to enhance the receptive field of our model. In this way, GAPN will simultaneously focus on a specific batch of complexes and the global assembly rules learned from complexes with varied chain numbers. Empirically, we have achieved both significant accuracy (measured by RMSD and TM-Score) and efficiency improvements compared to leading PCM softwares. GAPN outperforms the state-of-the-art method (MoLPC) with up to 27% improvement in TM-Score, with a speed-up of 600 times. Our code is released at \urlthis https URL.

ML-147-标题: ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation

链接: https://arxiv.org/abs/2405.02292
作者: ALOHA 2 Team, Jorge Aldaco, Travis Armstrong, Robert Baruch, Jeff Bingham, Sanky Chan, Kenneth Draper, Debidatta Dwibedi, Chelsea Finn, Pete Florence, Spencer Goodrich, Wayne Gramlich, Torr Hage, Alexander Herzog, Jonathan Hoech, Thinh Nguyen, Ian Storz, Baruch Tabanpour, Leila Takayama, Jonathan Tompson, Ayzaan Wahid, Ted Wahrburg, Sichun Xu, Sergey Yaroshenko, Kevin Zakka, Tony Z. Zhao
备注: Project website: aloha-2.github.io

点击查看摘要

Abstract:Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at this http URL.

ML-148-标题: Fault Detection and Monitoring using an Information-Driven Strategy: Method Theory and Application

链接: https://arxiv.org/abs/2405.03667
作者: Camilo Ramírez, Jorge F. Silva, Ferhat Tamssaouet, Tomás Rojas, Marcos E. Orchard
备注: 28 pages, 11 figures

点击查看摘要

Abstract:The ability to detect when a system undergoes an incipient fault is of paramount importance in preventing a critical failure. In this work, we propose an information-driven fault detection method based on a novel concept drift detector. The method is tailored to identifying drifts in input-output relationships of additive noise models (i.e., model drifts) and is based on a distribution-free mutual information (MI) estimator. Our scheme does not require prior faulty examples and can be applied distribution-free over a large class of system models. Our core contributions are twofold. First, we demonstrate the connection between fault detection, model drift detection, and testing independence between two random variables. Second, we prove several theoretical properties of the proposed MI-based fault detection scheme: (i) strong consistency, (ii) exponentially fast detection of the non-faulty case, and (iii) control of both significance levels and power of the test. To conclude, we validate our theory with synthetic data and the benchmark dataset N-CMAPSS of aircraft turbofan engines. These empirical results support the usefulness of our methodology in many practical and realistic settings, and the theoretical results show performance guarantees that other methods cannot offer.

ML-149-标题: Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models

链接: https://arxiv.org/abs/2405.03549
作者: Ludwig Winkler, Lorenz Richter, Manfred Opper
备注:

点击查看摘要

Abstract:Generative modeling via stochastic processes has led to remarkable empirical results as well as to recent advances in their theoretical understanding. In principle, both space and time of the processes can be discrete or continuous. In this work, we study time-continuous Markov jump processes on discrete state spaces and investigate their correspondence to state-continuous diffusion processes given by SDEs. In particular, we revisit the \textitEhrenfest process , which converges to an Ornstein-Uhlenbeck process in the infinite state space limit. Likewise, we can show that the time-reversal of the Ehrenfest process converges to the time-reversed Ornstein-Uhlenbeck process. This observation bridges discrete and continuous state spaces and allows to carry over methods from one to the respective other setting. Additionally, we suggest an algorithm for training the time-reversal of Markov jump processes which relies on conditional expectations and can thus be directly related to denoising score matching. We demonstrate our methods in multiple convincing numerical experiments.

ML-150-标题: Enhancing Channel Estimation in Quantized Systems with a Generative Prior

链接: https://arxiv.org/abs/2405.03542
作者: Benedikt Fesl, Aziz Banna, Wolfgang Utschick
备注:

点击查看摘要

Abstract:Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the EM algorithm as side information. Numerical results demonstrate the significant performance improvement of our proposed approach over both a simplistic Gaussian prior and current state-of-the-art channel estimators. Furthermore, the proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.

ML-151-标题: A Symplectic Analysis of Alternating Mirror Descent

链接: https://arxiv.org/abs/2405.03472
作者: Jonas Katona, Xiuyuan Wang, Andre Wibisono
备注: 95 pages, 3 figures

点击查看摘要

Abstract:Motivated by understanding the behavior of the Alternating Mirror Descent (AMD) algorithm for bilinear zero-sum games, we study the discretization of continuous-time Hamiltonian flow via the symplectic Euler method. We provide a framework for analysis using results from Hamiltonian dynamics, Lie algebra, and symplectic numerical integrators, with an emphasis on the existence and properties of a conserved quantity, the modified Hamiltonian (MH), for the symplectic Euler method. We compute the MH in closed-form when the original Hamiltonian is a quadratic function, and show that it generally differs from the other conserved quantity known previously in that case. We derive new error bounds on the MH when truncated at orders in the stepsize in terms of the number of iterations, K , and utilize this bound to show an improved \mathcalO(K^1/5) total regret bound and an \mathcalO(K^-4/5) duality gap of the average iterates for AMD. Finally, we propose a conjecture which, if true, would imply that the total regret for AMD goes as \mathcalO\left(K^\varepsilon\right) and the duality gap of the average iterates as \mathcalO\left(K^-1+\varepsilon\right) for any \varepsilon>0 , and we can take \varepsilon=0 upon certain convergence conditions for the MH.

ML-152-标题: Hierarchic Flows to Estimate and Sample High-dimensional Probabilities

链接: https://arxiv.org/abs/2405.03468
作者: Etienne Lempereur, Stéphane Mallat
备注:

点击查看摘要

Abstract:Finding low-dimensional interpretable models of complex physical fields such as turbulence remains an open question, 80 years after the pioneer work of Kolmogorov. Estimating high-dimensional probability distributions from data samples suffers from an optimization and an approximation curse of dimensionality. It may be avoided by following a hierarchic probability flow from coarse to fine scales. This inverse renormalization group is defined by conditional probabilities across scales, renormalized in a wavelet basis. For a \varphi^4 scalar potential, sampling these hierarchic models avoids the critical slowing down at the phase transition. An outstanding issue is to also approximate non-Gaussian fields having long-range interactions in space and across scales. We introduce low-dimensional models with robust multiscale approximations of high order polynomial energies. They are calculated with a second wavelet transform, which defines interactions over two hierarchies of scales. We estimate and sample these wavelet scattering models to generate 2D vorticity fields of turbulence, and images of dark matter densities.

ML-153-标题: A method for quantifying the generalization capabilities of generative models for solving Ising models

链接: https://arxiv.org/abs/2405.03435
作者: Qunlong Ma, Zhi Ma, Ming Gao
备注: 10 pages, 7 figures

点击查看摘要

Abstract:For Ising models with complex energy landscapes, whether the ground state can be found by neural networks depends heavily on the Hamming distance between the training datasets and the ground state. Despite the fact that various recently proposed generative models have shown good performance in solving Ising models, there is no adequate discussion on how to quantify their generalization capabilities. Here we design a Hamming distance regularizer in the framework of a class of generative models, variational autoregressive networks (VAN), to quantify the generalization capabilities of various network architectures combined with VAN. The regularizer can control the size of the overlaps between the ground state and the training datasets generated by networks, which, together with the success rates of finding the ground state, form a quantitative metric to quantify their generalization capabilities. We conduct numerical experiments on several prototypical network architectures combined with VAN, including feed-forward neural networks, recurrent neural networks, and graph neural networks, to quantify their generalization capabilities when solving Ising models. Moreover, considering the fact that the quantification of the generalization capabilities of networks on small-scale problems can be used to predict their relative performance on large-scale problems, our method is of great significance for assisting in the Neural Architecture Search field of searching for the optimal network architectures when solving large-scale Ising models.

ML-154-标题: Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up

链接: https://arxiv.org/abs/2405.03293
作者: Isidro Gómez-Vargas, J. Alberto Vázquez
备注:

点击查看摘要

Abstract:In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a robust framework for extracting theoretical insights from observational data. However, its computational demands can be substantial, primarily due to the need for numerous likelihood function evaluations. Our proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the likelihood function dynamically during the Bayesian inference process. Unlike traditional approaches, our method trains neural networks on-the-fly using the current set of live points as training data, without the need for pre-training. This flexibility enables adaptation to various theoretical models and datasets. We perform simple hyperparameter optimization using genetic algorithms to suggest initial neural network architectures for learning each likelihood function. Once sufficient accuracy is achieved, the neural network replaces the original likelihood function. The implementation integrates with nested sampling algorithms and has been thoroughly evaluated using both simple cosmological dark energy models and diverse observational datasets. Additionally, we explore the potential of genetic algorithms for generating initial live points within nested sampling inference, opening up new avenues for enhancing the efficiency and effectiveness of Bayesian inference methods.

ML-155-标题: Stability Evaluation via Distributional Perturbation Analysis ICML2024

链接: https://arxiv.org/abs/2405.03198
作者: Jose Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu
备注: Accepted by ICML 2024

点击查看摘要

Abstract:The performance of learning models often deteriorates when deployed in out-of-sample environments. To ensure reliable deployment, we propose a stability evaluation criterion based on distributional perturbations. Conceptually, our stability evaluation criterion is defined as the minimal perturbation required on our observed dataset to induce a prescribed deterioration in risk evaluation. In this paper, we utilize the optimal transport (OT) discrepancy with moment constraints on the \textit(sample, density) space to quantify this perturbation. Therefore, our stability evaluation criterion can address both \emphdata corruptions and \emphsub-population shifts – the two most common types of distribution shifts in real-world scenarios. To further realize practical benefits, we present a series of tractable convex formulations and computational methods tailored to different classes of loss functions. The key technical tool to achieve this is the strong duality theorem provided in this paper. Empirically, we validate the practical utility of our stability evaluation criterion across a host of real-world applications. These empirical studies showcase the criterion’s ability not only to compare the stability of different learning models and features but also to provide valuable guidelines and strategies to further improve models.

ML-156-标题: Braced Fourier Continuation and Regression for Anomaly Detection

链接: https://arxiv.org/abs/2405.03180
作者: Josef Sabuda
备注: 16 pages, 9 figures, associated Github link: this https URL

点击查看摘要

Abstract:In this work, the concept of Braced Fourier Continuation and Regression (BFCR) is introduced. BFCR is a novel and computationally efficient means of finding nonlinear regressions or trend lines in arbitrary one-dimensional data sets. The Braced Fourier Continuation (BFC) and BFCR algorithms are first outlined, followed by a discussion of the properties of BFCR as well as demonstrations of how BFCR trend lines may be used effectively for anomaly detection both within and at the edges of arbitrary one-dimensional data sets. Finally, potential issues which may arise while using BFCR for anomaly detection as well as possible mitigation techniques are outlined and discussed. All source code and example data sets are either referenced or available via GitHub, and all associated code is written entirely in Python.

ML-157-标题: Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

链接: https://arxiv.org/abs/2405.03130
作者: Demetrios Papakostas, Andrew Herren, P. Richard Hahn, Francisco Castillo
备注:

点击查看摘要

Abstract:Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between. Concurrently, the study and usage of neural networks has also grown profoundly (albeit at a far faster rate). What we aim to do in this blog write-up is demonstrate a Neural Network causal inference architecture. We develop a fully connected neural network implementation of the popular Bayesian Causal Forest algorithm, a state of the art tree based method for estimating heterogeneous treatment effects. We compare our implementation to existing neural network causal inference methodologies, showing improvements in performance in simulation settings. We apply our method to a dataset examining the effect of stress on sleep.

ML-158-标题: Bayesian optimization for stable properties amid processing fluctuations in sputter deposition

链接: https://arxiv.org/abs/2405.03092
作者: Ankit Shrivastava, Matias Kalaswad, Joyce O. Custer, David P. Adams, Habib N. Najm
备注:

点击查看摘要

Abstract:We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctuations during deposition. Thin films are pivotal in numerous technologies, including semiconductors and optical devices, where their properties are critical. Sputter deposition parameters, such as deposition power, vacuum chamber pressure, and working distance, influence physical properties like residual stress and resistance. Excessive stress and high resistance can impair device performance, necessitating the selection of optimal process parameters. Furthermore, these parameters should ensure the consistency and reliability of thin film properties, assisting in the reproducibility of the devices. However, exploring the multidimensional design space for process optimization is expensive. Bayesian optimization is ideal for optimizing inputs/parameters of general black-box functions without reliance on gradient information. We utilize Bayesian optimization to optimize deposition power and pressure using a custom-built objective function incorporating observed stress and resistance data. Additionally, we integrate prior knowledge of stress variation with pressure into the objective function to prioritize films least affected by stochastic variations. Our findings demonstrate that Bayesian optimization effectively explores the design space and identifies optimal parameter combinations meeting desired stress and resistance specifications.

ML-159-标题: Causal K-Means Clustering

链接: https://arxiv.org/abs/2405.03083
作者: Kwangho Kim, Jisu Kim, Edward H. Kennedy
备注:

点击查看摘要

Abstract:Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects. We propose a new solution to this problem: Causal k-Means Clustering, which harnesses the widely-used k-means clustering algorithm to uncover the unknown subgroup structure. Our problem differs significantly from the conventional clustering setup since the variables to be clustered are unknown counterfactual functions. We present a plug-in estimator which is simple and readily implementable using off-the-shelf algorithms, and study its rate of convergence. We also develop a new bias-corrected estimator based on nonparametric efficiency theory and double machine learning, and show that this estimator achieves fast root-n rates and asymptotic normality in large nonparametric models. Our proposed methods are especially useful for modern outcome-wide studies with multiple treatment levels. Further, our framework is extensible to clustering with generic pseudo-outcomes, such as partially observed outcomes or otherwise unknown functions. Finally, we explore finite sample properties via simulation, and illustrate the proposed methods in a study of treatment programs for adolescent substance abuse.

ML-160-标题: Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection

链接: https://arxiv.org/abs/2405.03063
作者: Jingbo Liu
备注:

点击查看摘要

Abstract:Suppose that we first apply the Lasso to a design matrix, and then update one of its columns. In general, the signs of the Lasso coefficients may change, and there is no closed-form expression for updating the Lasso solution exactly. In this work, we propose an approximate formula for updating a debiased Lasso coefficient. We provide general nonasymptotic error bounds in terms of the norms and correlations of a given design matrix’s columns, and then prove asymptotic convergence results for the case of a random design matrix with i.i.d.\ sub-Gaussian row vectors and i.i.d.\ Gaussian noise. Notably, the approximate formula is asymptotically correct for most coordinates in the proportional growth regime, under the mild assumption that each row of the design matrix is sub-Gaussian with a covariance matrix having a bounded condition number. Our proof only requires certain concentration and anti-concentration properties to control various error terms and the number of sign changes. In contrast, rigorously establishing distributional limit properties (e.g.\ Gaussian limits for the debiased Lasso) under similarly general assumptions has been considered open problem in the universality theory. As applications, we show that the approximate formula allows us to reduce the computation complexity of variable selection algorithms that require solving multiple Lasso problems, such as the conditional randomization test and a variant of the knockoff filter.

ML-161-标题: Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network

链接: https://arxiv.org/abs/2405.02783
作者: Wandi Xu, Wei Xie
备注: 11 pages, 2 figures

点击查看摘要

Abstract:To support mechanism online learning and facilitate digital twin development for biomanufacturing processes, this paper develops an efficient Bayesian inference approach for partially observed enzymatic stochastic reaction network (SRN), a fundamental building block of multi-scale bioprocess mechanistic model. To tackle the critical challenges brought by the nonlinear stochastic differential equations (SDEs)-based mechanistic model with partially observed state and having measurement error, an interpretable Bayesian updating linear noise approximation (LNA) metamodel, incorporating the structure information of the mechanistic model, is proposed to approximate the likelihood of observations. Then, an efficient posterior sampling approach is developed by utilizing the gradients of the derived likelihood to speed up the convergence of MCMC. The empirical study demonstrates that the proposed approach has a promising performance.

ML-162-标题: Prévisions météorologiques basées sur lintelligence artificielle : une révolution peut en cacher une autre

链接: https://arxiv.org/abs/2405.02679
作者: Zied Ben-Bouallegue, Mariana C A Clare, Matthieu Chevallier
备注: 8 pages, in French

点击查看摘要

Abstract:Artificial intelligence (AI), based on deep-learning algorithm using high-quality reanalysis datasets, is showing enormous potential for weather forecasting. In this context, the European Centre for Medium-Range Weather Forecasts (ECMWF) is developing a new forecasting system based on AI. Verification results of deterministic forecast for now are promising. However, the realism of weather forecasts based on AI is often questioned. Here, different types of realism are identified and we discuss, in particular, the relationship between structural realism and predictability of weather events. Furthermore, a statistical analysis of deterministic forecasts based on AI points to a realism/performance dilemma that a probabilistic approach should help to solve. – L’intelligence artificielle (IA) bouleverse aujourd’hui le monde de la prévision météorologique avec l’utilisation d’algorithmes d’apprentissage profond nourris par des champs de réanalyses. Dans ce contexte, le Centre Européen pour les Prévisions Météorologiques à Moyen Terme (CEPMMT) a décidé de développer un nouveau système de prévisions resposant sur l’IA. Ces prévisions, pour le moment de type déterministe, montrent des résultats prometteurs. Toutefois, le réalisme de ce type de prévisions reposant sur l’IA est souvent questionné. Ici, nous identifions différents types de réalisme et interrogeons notamment le rapport entre réalisme structurel et prévisibilité des évênements météorologiques. Une analyse statistique de prévisions déterministes reposant sur l’IA laisse apparaitre un dilemme réalisme/performance qu’une approche probabiliste devrait aider à résoudre.

ML-163-标题: Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units

链接: https://arxiv.org/abs/2405.02563
作者: Alan Wu, Tilendra Choudhary, Pulakesh Upadhyaya, Ayman Ali, Philip Yang, Rishikesan Kamaleswaran
备注: 9 pages

点击查看摘要

Abstract:Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identify distinct groups of clinical trajectories of septic patients with ARF. For this retrospective study, we created a dataset from electronic medical records (EMR) consisting of data from sepsis patients admitted to medical intensive care units who required at least 24 hours of invasive mechanical ventilation at a quarternary care academic hospital in southeast USA for the years 2016-2021. A total of N=3349 patient encounters were included in this study. Clustering Representation Learning on Incomplete Time Series Data (CRLI) algorithm was applied to a parsimonious set of EMR variables in this data set. To validate the optimal number of clusters, the K-means algorithm was used in conjunction with dynamic time warping. Our model yielded four distinct patient phenotypes that were characterized as liver dysfunction/heterogeneous, hypercapnia, hypoxemia, and multiple organ dysfunction syndrome by a critical care expert. A Kaplan-Meier analysis to compare the 28-day mortality trends exhibited significant differences (p < 0.005) between the four phenotypes. The study demonstrates the utility of our deep representation learning-based approach in unraveling phenotypes that reflect the heterogeneity in sepsis-induced ARF in terms of different mortality outcomes and severity. These phenotypes might reveal important clinical insights into an effective prognosis and tailored treatment strategies.

ML-164-标题: Prediction of Space Weather Events through Analysis of Active Region Magnetograms using Convolutional Neural Network

链接: https://arxiv.org/abs/2405.02545
作者: Shlesh Sakpal
备注: 6 pages, 12 figures

点击查看摘要

Abstract:Although space weather events may not directly affect human life, they have the potential to inflict significant harm upon our communities. Harmful space weather events can trigger atmospheric changes that result in physical and economic damages on a global scale. In 1989, Earth experienced the effects of a powerful geomagnetic storm that caused satellites to malfunction, while triggering power blackouts in Canada, along with electricity disturbances in the United States and Europe. With the solar cycle peak rapidly approaching, there is an ever-increasing need to prepare and prevent the damages that can occur, especially to modern-day technology, calling for the need of a comprehensive prediction system. This study aims to leverage machine learning techniques to predict instances of space weather (solar flares, coronal mass ejections, geomagnetic storms), based on active region magnetograms of the Sun. This was done through the use of the NASA DONKI service to determine when these solar events occur, then using data from the NASA Solar Dynamics Observatory to compile a dataset that includes magnetograms of active regions of the Sun 24 hours before the events. By inputting the magnetograms into a convolutional neural network (CNN) trained from this dataset, it can serve to predict whether a space weather event will occur, and what type of event it will be. The model was designed using a custom architecture CNN, and returned an accuracy of 90.27%, a precision of 85.83%, a recall of 91.78%, and an average F1 score of 92.14% across each class (Solar flare [Flare], geomagnetic storm [GMS], coronal mass ejection [CME]). Our results show that using magnetogram data as an input for a CNN is a viable method to space weather prediction. Future work can involve prediction of the magnitude of solar events.

ML-165-标题: Modelling Sampling Distributions of Test Statistics with Autograd

链接: https://arxiv.org/abs/2405.02488
作者: Ali Al Kadhim, Harrison B. Prosper
备注:

点击查看摘要

Abstract:Simulation-based inference methods that feature correct conditional coverage of confidence sets based on observations that have been compressed to a scalar test statistic require accurate modelling of either the p-value function or the cumulative distribution function (cdf) of the test statistic. If the model of the cdf, which is typically a deep neural network, is a function of the test statistic then the derivative of the neural network with respect to the test statistic furnishes an approximation of the sampling distribution of the test statistic. We explore whether this approach to modelling conditional 1-dimensional sampling distributions is a viable alternative to the probability density-ratio method, also known as the likelihood-ratio trick. Relatively simple, yet effective, neural network models are used whose predictive uncertainty is quantified through a variety of methods.

ML-166-标题: Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning

链接: https://arxiv.org/abs/2405.02456
作者: Sihan Zeng, Thinh T. Doan, Justin Romberg
备注:

点击查看摘要

Abstract:Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves multiple tasks at the same time. This paper presents a constrained formulation for multi-task RL where the goal is to maximize the average performance of the policy across tasks subject to bounds on the performance in each task. We consider solving this problem both in the centralized setting, where information for all tasks is accessible to a single server, and in the decentralized setting, where a network of agents, each given one task and observing local information, cooperate to find the solution of the globally constrained objective using local communication. We first propose a primal-dual algorithm that provably converges to the globally optimal solution of this constrained formulation under exact gradient evaluations. When the gradient is unknown, we further develop a sampled-based actor-critic algorithm that finds the optimal policy using online samples of state, action, and reward. Finally, we study the extension of the algorithm to the linear function approximation setting.

ML-167-标题: Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design ICML2024 KR

链接: https://arxiv.org/abs/2405.02449
作者: Quan Nguyen, Adji Bousso Dieng
备注: Published in International Conference on Machine Learning, ICML 2024. Code can be found in the Vertaix GitHub: this https URL Paper dedicated to Kwame Nkrumah

点击查看摘要

Abstract:Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniques tend to favor exploitation over exploration of the search space, which causes them to get stuck in local optima. This ``collapse" problem prevents experimental design algorithms from yielding diverse high-quality data. In this paper, we extend the Vendi scores – a family of interpretable similarity-based diversity metrics – to account for quality. We then leverage these quality-weighted Vendi scores to tackle experimental design problems across various applications, including drug discovery, materials discovery, and reinforcement learning. We found that quality-weighted Vendi scores allow us to construct policies for experimental design that flexibly balance quality and diversity, and ultimately assemble rich and diverse sets of high-performing data points. Our algorithms led to a 70%-170% increase in the number of effective discoveries compared to baselines.

ML-168-标题: Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL

链接: https://arxiv.org/abs/2405.02374
作者: Arturo Fiorellini-Bernardis, Sebastien Boyer, Christoph Brunken, Bakary Diallo, Karim Beguir, Nicolas Lopez-Carranza, Oliver Bent
备注:

点击查看摘要

Abstract:Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is fundamental for modelling and re-engineering biological systems. Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. With this contribution, we propose eGRAL, a novel SE(3) equivariant graph neural network (eGNN) architecture designed for predicting binding affinity changes from multiple amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models. To address the limited availability of large-scale affinity assays with structural information, we generate a simulated dataset comprising approximately 500,000 data points. Our model is pre-trained on this dataset, then fine-tuned and tested on experimental data.

ML-169-标题: Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints

链接: https://arxiv.org/abs/2405.02373
作者: Ahmed Sid-Ali, Ioannis Lambadaris, Yiqiang Q. Zhao, Gennady Shaikhet, Amirhossein Asgharnia
备注: arXiv admin note: text overlap with arXiv:2305.15558

点击查看摘要

Abstract:This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it.

ML-170-标题: Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness Optimality and Convergence ICML2024

链接: https://arxiv.org/abs/2405.02372
作者: Yancheng Huang, Kai Yang, Zelin Zhu, Leian Chen
备注: Accepted at ICML2024

点击查看摘要

Abstract:The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the parameters linked to the data stream. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism also could mitigate the straggler issue that faced by traditional synchronous algorithm. We then analyze the non-asymptotic convergence property of triadic-OCD and derive its iteration complexity to achieve an \epsilon -optimal point. Finally, extensive experiments have been conducted to elucidate the effectiveness of the proposed method.

ML-171-标题: Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies

链接: https://arxiv.org/abs/2405.02366
作者: Jonathan Serrano-Pérez, Raquel Díaz Hernández, L. Enrique Sucar
备注:

点击查看摘要

Abstract:This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a convolutional neural network (CNN) is trained with images of the different classes of galaxies (image augmentation is carried out to balance some classes); the CNN outputs the probability for each class of the hierarchy, and its outputs/predictions feed the second module. The second module consists of a Bayesian network that represents the hierarchy and helps to improve the prediction accuracy by combining the predictions of the first phase while maintaining the hierarchical constraint (in a hierarchy, an instance associated with a node must be associated to all its ancestors), through probabilistic inference over the Bayesian network so that a consistent prediction is obtained. Different images from the Hubble telescope have been collected and labeled by experts, which are used to perform the experiments. The results show that BCNN performed better than several CNNs in multiple evaluation measures, reaching the next scores: 67% in exact match, 78% in accuracy, and 83% in hierarchical F-measure.

ML-172-标题: A Comprehensive Approach to Carbon Dioxide Emission Analysis in High Human Development Index Countries using Statistical and Machine Learning Techniques

链接: https://arxiv.org/abs/2405.02340
作者: Hamed Khosravi, Ahmed Shoyeb Raihan, Farzana Islam, Ashish Nimbarte, Imtiaz Ahmed
备注:

点击查看摘要

Abstract:Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emission, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It’s imperative to forecast CO2 emission trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emission. This paper presents an in-depth comparative study on the determinants of CO2 emission in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emission. Following this, the study leverages supervised and unsupervised machine learning (ML) methods to further scrutinize and understand the factors influencing CO2 emission. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a supervised ML model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised learning approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emission predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change.

计算机视觉

CV-0-标题: Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs

链接: https://arxiv.org/abs/2405.03690
作者: Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Jameel Hassan, Muzammal Naseer, Federico Tombari, Fahad Shahbaz Khan, Salman Khan
备注: Technical report

点击查看摘要

Abstract:Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding tasks. These models have the potential to be deployed in real-world applications such as robotics, AI assistants, medical imaging, and autonomous vehicles. The widespread adoption of Video-LMMs in our daily lives underscores the importance of ensuring and evaluating their robust performance in mirroring human-like reasoning and interaction capabilities in complex, real-world contexts. However, existing benchmarks for Video-LMMs primarily focus on general video comprehension abilities and neglect assessing their reasoning capabilities over complex videos in the real-world context, and robustness of these models through the lens of user prompts as text queries. In this paper, we present the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), a novel benchmark that comprehensively assesses the performance of Video-LMMs across 11 diverse real-world video dimensions. We evaluate 9 recent models, including both open-source and closed-source variants, and find that most of the Video-LMMs, especially open-source ones, struggle with robustness and reasoning when dealing with complex videos. Based on our analysis, we develop a training-free Dual-Step Contextual Prompting (DSCP) technique to enhance the performance of existing Video-LMMs. Our findings provide valuable insights for building the next generation of human-centric AI systems with advanced robustness and reasoning capabilities. Our dataset and code are publicly available at: this https URL.

CV-1-标题: An Empty Room is All We Want: Automatic Defurnishing of Indoor Panoramas CVPR2024

链接: https://arxiv.org/abs/2405.03682
作者: Mira Slavcheva, Dave Gausebeck, Kevin Chen, David Buchhofer, Azwad Sabik, Chen Ma, Sachal Dhillon, Olaf Brandt, Alan Dolhasz
备注: Accepted at CVPR 2024 workshops. Project page: this https URL

点击查看摘要

Abstract:We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing – the removal of furniture items from indoor panorama images. Specifically, we illustrate how increased context, domain-specific model fine-tuning, and improved image blending can produce high-fidelity inpaints that are geometrically plausible without needing to rely on room layout estimation. We demonstrate qualitative and quantitative improvements over other furniture removal techniques.

CV-2-标题: MemoryMamba: Memory-Augmented State Space Model for Defect Recognition

链接: https://arxiv.org/abs/2405.03673
作者: Qianning Wang, He Hu, Yucheng Zhou
备注: 15 pages, 7 figures

点击查看摘要

Abstract:As automation advances in manufacturing, the demand for precise and sophisticated defect detection technologies grows. Existing vision models for defect recognition methods are insufficient for handling the complexities and variations of defects in contemporary manufacturing settings. These models especially struggle in scenarios involving limited or imbalanced defect data. In this work, we introduce MemoryMamba, a novel memory-augmented state space model (SSM), designed to overcome the limitations of existing defect recognition models. MemoryMamba integrates the state space model with the memory augmentation mechanism, enabling the system to maintain and retrieve essential defect-specific information in training. Its architecture is designed to capture dependencies and intricate defect characteristics, which are crucial for effective defect detection. In the experiments, MemoryMamba was evaluated across four industrial datasets with diverse defect types and complexities. The model consistently outperformed other methods, demonstrating its capability to adapt to various defect recognition scenarios.

CV-3-标题: Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation

链接: https://arxiv.org/abs/2405.03662
作者: Dong Lao, Congli Wang, Alex Wong, Stefano Soatto
备注:

点击查看摘要

Abstract:We describe a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. Since supervised data is often technically impossible to obtain, assumptions and biases have to be imposed to solve this inverse problem, and we choose to model them explicitly. Rather than initializing a latent irradiance (“template”) by heuristics to estimate deformation, we select one of the images as a reference, and model the deformation in this image by the aggregation of the optical flow from it to other images, exploiting a prior imposed by Central Limit Theorem. Then with a novel flow inversion module, the model registers each image TO the template but WITHOUT the template, avoiding artifacts related to poor template initialization. To illustrate the robustness of the method, we simply (i) select the first frame as the reference and (ii) use the simplest optical flow to estimate the warpings, yet the improvement in registration is decisive in the final reconstruction, as we achieve state-of-the-art performance despite its simplicity. The method establishes a strong baseline that can be further improved by integrating it seamlessly into more sophisticated pipelines, or with domain-specific methods if so desired.

CV-4-标题: CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification ICDAR2024

链接: https://arxiv.org/abs/2405.03660
作者: Sankalp Sinha, Muhammad Saif Ullah Khan, Talha Uddin Sheikh, Didier Stricker, Muhammad Zeshan Afzal
备注: 18 Pages, 4 Figures and Accepted in ICDAR 2024

点击查看摘要

Abstract:Zero-shot learning has been extensively investigated in the broader field of visual recognition, attracting significant interest recently. However, the current work on zero-shot learning in document image classification remains scarce. The existing studies either focus exclusively on zero-shot inference, or their evaluation does not align with the established criteria of zero-shot evaluation in the visual recognition domain. We provide a comprehensive document image classification analysis in Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) settings to address this gap. Our methodology and evaluation align with the established practices of this domain. Additionally, we propose zero-shot splits for the RVL-CDIP dataset. Furthermore, we introduce CICA (pronounced ‘ki-ka’), a framework that enhances the zero-shot learning capabilities of CLIP. CICA consists of a novel ‘content module’ designed to leverage any generic document-related textual information. The discriminative features extracted by this module are aligned with CLIP’s text and image features using a novel ‘coupled-contrastive’ loss. Our module improves CLIP’s ZSL top-1 accuracy by 6.7% and GZSL harmonic mean by 24% on the RVL-CDIP dataset. Our module is lightweight and adds only 3.3% more parameters to CLIP. Our work sets the direction for future research in zero-shot document classification.

CV-5-标题: A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose

链接: https://arxiv.org/abs/2405.03659
作者: Kaiwen Jiang, Yang Fu, Mukund Varma T, Yash Belhe, Xiaolong Wang, Hao Su, Ravi Ramamoorthi
备注:

点击查看摘要

Abstract:Novel view synthesis from a sparse set of input images is a challenging problem of great practical interest, especially when camera poses are absent or inaccurate. Direct optimization of camera poses and usage of estimated depths in neural radiance field algorithms usually do not produce good results because of the coupling between poses and depths, and inaccuracies in monocular depth estimation. In this paper, we leverage the recent 3D Gaussian splatting method to develop a novel construct-and-optimize method for sparse view synthesis without camera poses. Specifically, we construct a solution progressively by using monocular depth and projecting pixels back into the 3D world. During construction, we optimize the solution by detecting 2D correspondences between training views and the corresponding rendered images. We develop a unified differentiable pipeline for camera registration and adjustment of both camera poses and depths, followed by back-projection. We also introduce a novel notion of an expected surface in Gaussian splatting, which is critical to our optimization. These steps enable a coarse solution, which can then be low-pass filtered and refined using standard optimization methods. We demonstrate results on the Tanks and Temples and Static Hikes datasets with as few as three widely-spaced views, showing significantly better quality than competing methods, including those with approximate camera pose information. Moreover, our results improve with more views and outperform previous InstantNGP and Gaussian Splatting algorithms even when using half the dataset.

CV-6-标题: Field-of-View Extension for Diffusion MRI via Deep Generative Models

链接: https://arxiv.org/abs/2405.03652
作者: Chenyu Gao, Shunxing Bao, Michael Kim, Nancy Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter Kukull, Arthur Toga, Derek Archer, Timothy Hohman, Bennett Landman, Zhiyuan Li
备注: 20 pages, 11 figures

点击查看摘要

Abstract:Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (FOV). This work aims to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with complete FOV can improve the whole-brain tractography for corrupted data with incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWI) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWI outside of incomplete FOV. Results: For evaluating the imputed slices, on the WRAP dataset the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, SSIMb1300=0.893; on the NACC dataset it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, SSIMb1300= 0.877. The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts (p < 0.001) on both the WRAP and NACC datasets. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in dMRI data with incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer’s Disease.

CV-7-标题: Generated Contents Enrichment

链接: https://arxiv.org/abs/2405.03650
作者: Mahdi Naseri, Jiayan Qiu, Zhou Wang
备注:

点击查看摘要

Abstract:In this paper, we investigate a novel artificial intelligence generation task, termed as generated contents enrichment (GCE). Different from conventional artificial intelligence contents generation task that enriches the given textual description implicitly with limited semantics for generating visually real content, our proposed GCE strives to perform content enrichment explicitly on both the visual and textual domain, from which the enriched contents are visually real, structurally reasonable, and semantically abundant. Towards to solve GCE, we propose a deep end-to-end method that explicitly explores the semantics and inter-semantic relationships during the enrichment. Specifically, we first model the input description as a semantic graph, wherein each node represents an object and each edge corresponds to the inter-object relationship. We then adopt Graph Convolutional Networks on top of the input scene description to predict the enriching objects and their relationships with the input objects. Finally, the enriched graph is fed into an image synthesis model to carry out the visual contents generation. Our experiments conducted on the Visual Genome dataset exhibit promising and visually plausible results.

CV-8-标题: Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation IJCAI2024

链接: https://arxiv.org/abs/2405.03649
作者: Guangtao Zheng, Wenqian Ye, Aidong Zhang
备注: Accepted to IJCAI 2024

点击查看摘要

Abstract:Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization capability. Training classifiers robust to spurious correlations typically relies on annotations of spurious correlations in data, which are often expensive to get. In this paper, we tackle an annotation-free setting and propose a self-guided spurious correlation mitigation framework. Our framework automatically constructs fine-grained training labels tailored for a classifier obtained with empirical risk minimization to improve its robustness against spurious correlations. The fine-grained training labels are formulated with different prediction behaviors of the classifier identified in a novel spuriousness embedding space. We construct the space with automatically detected conceptual attributes and a novel spuriousness metric which measures how likely a class-attribute correlation is exploited for predictions. We demonstrate that training the classifier to distinguish different prediction behaviors reduces its reliance on spurious correlations without knowing them a priori and outperforms prior methods on five real-world datasets.

CV-9-标题: Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors

链接: https://arxiv.org/abs/2405.03643
作者: Samreen Anjum, Suyog Jain, Danna Gurari
备注:

点击查看摘要

Abstract:We propose a hybrid framework for consistently producing high-quality object tracks by combining an automated object tracker with little human input. The key idea is to tailor a module for each dataset to intelligently decide when an object tracker is failing and so humans should be brought in to re-localize an object for continued tracking. Our approach leverages self-supervised learning on unlabeled videos to learn a tailored representation for a target object that is then used to actively monitor its tracked region and decide when the tracker fails. Since labeled data is not needed, our approach can be applied to novel object categories. Experiments on three datasets demonstrate our method outperforms existing approaches, especially for small, fast moving, or occluded objects.

CV-10-标题: Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method

链接: https://arxiv.org/abs/2405.03642
作者: Matina Mahdizadeh Sani, Ali Royat, Mahdieh Soleymani Baghshah
备注: 16 pages, 3 figures, 4 tables

点击查看摘要

Abstract:Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method to reduce the impact of false positives. Unlike most existing methods that rely predominantly on fully supervised learning, our approach leverages the advantages of self-supervised learning in conjunction with employing the available labeled data. We evaluate our method on the BreakHis dataset, which consists of breast cancer histopathology images, and demonstrate an increase in classification accuracy by 1.45% at the image level and 1.42% at the patient level compared to the state-of-the-art method. This improvement corresponds to 93.63% absolute accuracy, highlighting our approach’s effectiveness in leveraging data properties to learn more appropriate representation space.

CV-11-标题: Neural Graph Mapping for Dense SLAM with Efficient Loop Closure

链接: https://arxiv.org/abs/2405.03633
作者: Leonard Bruns, Jun Zhang, Patric Jensfelt
备注: Project page: this https URL

点击查看摘要

Abstract:Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation. This prevents efficient incorporation of loop closure constraints and limits scalability. To address these shortcomings, we propose a neural mapping framework which anchors lightweight neural fields to the pose graph of a sparse visual SLAM system. Our approach shows the ability to integrate large-scale loop closures, while limiting necessary reintegration. Furthermore, we verify the scalability of our approach by demonstrating successful building-scale mapping taking multiple loop closures into account during the optimization, and show that our method outperforms existing state-of-the-art approaches on large scenes in terms of quality and runtime. Our code is available at this https URL.

CV-12-标题: Dual Relation Mining Network for Zero-Shot Learning

链接: https://arxiv.org/abs/2405.03613
作者: Jinwei Han, Yingguo Gao, Zhiwen Lin, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia
备注:

点击查看摘要

Abstract:Zero-shot learning (ZSL) aims to recognize novel classes through transferring shared semantic knowledge (e.g., attributes) from seen classes to unseen classes. Recently, attention-based methods have exhibited significant progress which align visual features and attributes via a spatial attention mechanism. However, these methods only explore visual-semantic relationship in the spatial dimension, which can lead to classification ambiguity when different attributes share similar attention regions, and semantic relationship between attributes is rarely discussed. To alleviate the above problems, we propose a Dual Relation Mining Network (DRMN) to enable more effective visual-semantic interactions and learn semantic relationship among attributes for knowledge transfer. Specifically, we introduce a Dual Attention Block (DAB) for visual-semantic relationship mining, which enriches visual information by multi-level feature fusion and conducts spatial attention for visual to semantic embedding. Moreover, an attribute-guided channel attention is utilized to decouple entangled semantic features. For semantic relationship modeling, we utilize a Semantic Interaction Transformer (SIT) to enhance the generalization of attribute representations among images. Additionally, a global classification branch is introduced as a complement to human-defined semantic attributes, and we then combine the results with attribute-based classification. Extensive experiments demonstrate that the proposed DRMN leads to new state-of-the-art performances on three standard ZSL benchmarks, i.e., CUB, SUN, and AwA2.

CV-13-标题: Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing AAAI2024

链接: https://arxiv.org/abs/2405.03565
作者: Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Wei Wang, Fenglong Ma, Hongyang Chen, Hong Yu, Xianchao Zhang
备注: Accepted to AAAI 2024

点击查看摘要

Abstract:Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all. While prevailing methods have shown promising performance via transferring knowledge from seen classes to unseen classes, they are still limited by (1) Inherent dissimilarities among classes make the transformation of features learned from seen classes to unseen classes both difficult and inefficient. (2) Rare labeled novel samples usually cannot provide enough supervision signals to enable the model to adjust from the source distribution to the target distribution, especially for complicated scenarios. To alleviate the above issues, we propose a simple and effective strategy for few-shot and zero-shot text classification. We aim to liberate the model from the confines of seen classes, thereby enabling it to predict unseen categories without the necessity of training on seen classes. Specifically, for mining more related unseen category knowledge, we utilize a large pre-trained language model to generate pseudo novel samples, and select the most representative ones as category anchors. After that, we convert the multi-class classification task into a binary classification task and use the similarities of query-anchor pairs for prediction to fully leverage the limited supervision signals. Extensive experiments on six widely used public datasets show that our proposed method can outperform other strong baselines significantly in few-shot and zero-shot tasks, even without using any seen class samples.

CV-14-标题: CCDM: Continuous Conditional Diffusion Models for Image Generation

链接: https://arxiv.org/abs/2405.03546
作者: Xin Ding, Yongwei Wang, Kao Zhang, Z. Jane Wang
备注:

点击查看摘要

Abstract:Continuous Conditional Generative Modeling (CCGM) aims to estimate the distribution of high-dimensional data, typically images, conditioned on scalar continuous variables known as regression labels. While Continuous conditional Generative Adversarial Networks (CcGANs) were initially designed for this task, their adversarial training mechanism remains vulnerable to extremely sparse or imbalanced data, resulting in suboptimal outcomes. To enhance the quality of generated images, a promising alternative is to replace CcGANs with Conditional Diffusion Models (CDMs), renowned for their stable training process and ability to produce more realistic images. However, existing CDMs encounter challenges when applied to CCGM tasks due to several limitations such as inadequate U-Net architectures and deficient model fitting mechanisms for handling regression labels. In this paper, we introduce Continuous Conditional Diffusion Models (CCDMs), the first CDM designed specifically for the CCGM task. CCDMs address the limitations of existing CDMs by introducing specially designed conditional diffusion processes, a modified denoising U-Net with a custom-made conditioning mechanism, a novel hard vicinal loss for model fitting, and an efficient conditional sampling procedure. With comprehensive experiments on four datasets with varying resolutions ranging from 64x64 to 192x192, we demonstrate the superiority of the proposed CCDM over state-of-the-art CCGM models, establishing new benchmarks in CCGM. Extensive ablation studies validate the model design and implementation configuration of the proposed CCDM. Our code is publicly available at this https URL.

CV-15-标题: Optimizing Hand Region Detection in MediaPipe Holistic Full-Body Pose Estimation to Improve Accuracy and Avoid Downstream Errors

链接: https://arxiv.org/abs/2405.03545
作者: Amit Moryossef
备注:

点击查看摘要

Abstract:This paper addresses a critical flaw in MediaPipe Holistic’s hand Region of Interest (ROI) prediction, which struggles with non-ideal hand orientations, affecting sign language recognition accuracy. We propose a data-driven approach to enhance ROI estimation, leveraging an enriched feature set including additional hand keypoints and the z-dimension. Our results demonstrate better estimates, with higher Intersection-over-Union compared to the current method. Our code and optimizations are available at this https URL.

CV-16-标题: RepVGG-GELAN: Enhanced GELAN with VGG-STYLE ConvNets for Brain Tumour Detection

链接: https://arxiv.org/abs/2405.03541
作者: Thennarasi Balakrishnan, Sandeep Singh Sengar
备注:

点击查看摘要

Abstract:Object detection algorithms particularly those based on YOLO have demonstrated remarkable efficiency in balancing speed and accuracy. However, their application in brain tumour detection remains underexplored. This study proposes RepVGG-GELAN, a novel YOLO architecture enhanced with RepVGG, a reparameterized convolutional approach for object detection tasks particularly focusing on brain tumour detection within medical images. RepVGG-GELAN leverages the RepVGG architecture to improve both speed and accuracy in detecting brain tumours. Integrating RepVGG into the YOLO framework aims to achieve a balance between computational efficiency and detection performance. This study includes a spatial pyramid pooling-based Generalized Efficient Layer Aggregation Network (GELAN) architecture which further enhances the capability of RepVGG. Experimental evaluation conducted on a brain tumour dataset demonstrates the effectiveness of RepVGG-GELAN surpassing existing RCS-YOLO in terms of precision and speed. Specifically, RepVGG-GELAN achieves an increased precision of 4.91% and an increased AP50 of 2.54% over the latest existing approach while operating at 240.7 GFLOPs. The proposed RepVGG-GELAN with GELAN architecture presents promising results establishing itself as a state-of-the-art solution for accurate and efficient brain tumour detection in medical images. The implementation code is publicly available at this https URL.

CV-17-标题: Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond DATE

链接: https://arxiv.org/abs/2405.03520
作者: Zheng Zhu, Xiaofeng Wang, Wangbo Zhao, Chen Min, Nianchen Deng, Min Dou, Yuqi Wang, Botian Shi, Kai Wang, Chi Zhang, Yang You, Zhaoxiang Zhang, Dawei Zhao, Liang Xiao, Jian Zhao, Jiwen Lu, Guan Huang
备注: This survey will be regularly updated at: this https URL

点击查看摘要

Abstract:General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems. Recently, the emergence of the Sora model has attained significant attention due to its remarkable simulation capabilities, which exhibits an incipient comprehension of physical laws. In this survey, we embark on a comprehensive exploration of the latest advancements in world models. Our analysis navigates through the forefront of generative methodologies in video generation, where world models stand as pivotal constructs facilitating the synthesis of highly realistic visual content. Additionally, we scrutinize the burgeoning field of autonomous-driving world models, meticulously delineating their indispensable role in reshaping transportation and urban mobility. Furthermore, we delve into the intricacies inherent in world models deployed within autonomous agents, shedding light on their profound significance in enabling intelligent interactions within dynamic environmental contexts. At last, we examine challenges and limitations of world models, and discuss their potential future directions. We hope this survey can serve as a foundational reference for the research community and inspire continued innovation. This survey will be regularly updated at: this https URL.

CV-18-标题: Low-light Object Detection

链接: https://arxiv.org/abs/2405.03519
作者: Pengpeng Li, Haowei Gu, Yang Yang
备注:

点击查看摘要

Abstract:In this competition we employed a model fusion approach to achieve object detection results close to those of real images. Our method is based on the CO-DETR model, which was trained on two sets of data: one containing images under dark conditions and another containing images enhanced with low-light conditions. We used various enhancement techniques on the test data to generate multiple sets of prediction results. Finally, we applied a clustering aggregation method guided by IoU thresholds to select the optimal results.

CV-19-标题: Boosting Single Positive Multi-label Classification with Generalized Robust Loss

链接: https://arxiv.org/abs/2405.03501
作者: Yanxi Chen, Chunxiao Li, Xinyang Dai, Jinhuan Li, Weiyu Sun, Yiming Wang, Renyuan Zhang, Tinghe Zhang, Bo Wang
备注: 14 pages, 5 figures, 6 tables

点击查看摘要

Abstract:Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate Single Positive Multi-label Learning (SPML), where each image is associated with merely one positive label. Existing SPML methods only focus on designing losses using mechanisms such as hard pseudo-labeling and robust losses, mostly leading to unacceptable false negatives. To address this issue, we first propose a generalized loss framework based on expected risk minimization to provide soft pseudo labels, and point out that the former losses can be seamlessly converted into our framework. In particular, we design a novel robust loss based on our framework, which enjoys flexible coordination between false positives and false negatives, and can additionally deal with the imbalance between positive and negative samples. Extensive experiments show that our approach can significantly improve SPML performance and outperform the vast majority of state-of-the-art methods on all the four benchmarks.

CV-20-标题: A Rate-Distortion-Classification Approach for Lossy Image Compression

链接: https://arxiv.org/abs/2405.03500
作者: Yuefeng Zhang
备注: 15 pages

点击查看摘要

Abstract:In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to optimize the trade-off between rate, distortion, and classification accuracy. The RDC model is extensively analyzed both statistically on a multi-distribution source and experimentally on the widely used MNIST dataset. The findings reveal that the RDC model exhibits desirable properties, including monotonic non-increasing and convex functions, under certain conditions. This work provides insights into the development of human-machine friendly compression methods and Video Coding for Machine (VCM) approaches, paving the way for end-to-end image compression techniques in real-world applications.

CV-21-标题: UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images

链接: https://arxiv.org/abs/2405.03486
作者: Yiting Qu, Xinyue Shen, Yixin Wu, Michael Backes, Savvas Zannettou, Yang Zhang
备注:

点击查看摘要

Abstract:Image safety classifiers play an important role in identifying and mitigating the spread of unsafe images online (e.g., images including violence, hateful rhetoric, etc.). At the same time, with the advent of text-to-image models and increasing concerns about the safety of AI models, developers are increasingly relying on image safety classifiers to safeguard their models. Yet, the performance of current image safety classifiers remains unknown for real-world and AI-generated images. To bridge this research gap, in this work, we propose UnsafeBench, a benchmarking framework that evaluates the effectiveness and robustness of image safety classifiers. First, we curate a large dataset of 10K real-world and AI-generated images that are annotated as safe or unsafe based on a set of 11 unsafe categories of images (sexual, violent, hateful, etc.). Then, we evaluate the effectiveness and robustness of five popular image safety classifiers, as well as three classifiers that are powered by general-purpose visual language models. Our assessment indicates that existing image safety classifiers are not comprehensive and effective enough in mitigating the multifaceted problem of unsafe images. Also, we find that classifiers trained only on real-world images tend to have degraded performance when applied to AI-generated images. Motivated by these findings, we design and implement a comprehensive image moderation tool called PerspectiveVision, which effectively identifies 11 categories of real-world and AI-generated unsafe images. The best PerspectiveVision model achieves an overall F1-Score of 0.810 on six evaluation datasets, which is comparable with closed-source and expensive state-of-the-art models like GPT-4V. UnsafeBench and PerspectiveVision can aid the research community in better understanding the landscape of image safety classification in the era of generative AI.

CV-22-标题: LGTM: Local-to-Global Text-Driven Human Motion Diffusion Model SIGGRAPH2024

链接: https://arxiv.org/abs/2405.03485
作者: Haowen Sun, Ruikun Zheng, Haibin Huang, Chongyang Ma, Hui Huang, Ruizhen Hu
备注: 9 pages,7 figures, SIGGRAPH 2024

点击查看摘要

Abstract:In this paper, we introduce LGTM, a novel Local-to-Global pipeline for Text-to-Motion generation. LGTM utilizes a diffusion-based architecture and aims to address the challenge of accurately translating textual descriptions into semantically coherent human motion in computer animation. Specifically, traditional methods often struggle with semantic discrepancies, particularly in aligning specific motions to the correct body parts. To address this issue, we propose a two-stage pipeline to overcome this challenge: it first employs large language models (LLMs) to decompose global motion descriptions into part-specific narratives, which are then processed by independent body-part motion encoders to ensure precise local semantic alignment. Finally, an attention-based full-body optimizer refines the motion generation results and guarantees the overall coherence. Our experiments demonstrate that LGTM gains significant improvements in generating locally accurate, semantically-aligned human motion, marking a notable advancement in text-to-motion applications. Code and data for this paper are available at this https URL

CV-23-标题: A Lightweight Neural Architecture Search Model for Medical Image Classification

链接: https://arxiv.org/abs/2405.03462
作者: Lunchen Xie, Eugenio Lomurno, Matteo Gambella, Danilo Ardagna, Manuel Roveri, Matteo Matteucci, Qingjiang Shi
备注:

点击查看摘要

Abstract:Accurate classification of medical images is essential for modern diagnostics. Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and more accurate decisions, sometimes replacing human judgment. However, model development is costly and repetitive. Neural Architecture Search (NAS) provides solutions by automating the design of deep learning architectures. This paper presents ZO-DARTS+, a differentiable NAS algorithm that improves search efficiency through a novel method of generating sparse probabilities by bi-level optimization. Experiments on five public medical datasets show that ZO-DARTS+ matches the accuracy of state-of-the-art solutions while reducing search times by up to three times.

CV-24-标题: SSyncOA: Self-synchronizing Object-aligned Watermarking to Resist Cropping-paste Attacks ICME2024

链接: https://arxiv.org/abs/2405.03458
作者: Chengxin Zhao, Hefei Ling, Sijing Xie, Han Fang, Yaokun Fang, Nan Sun
备注: 7 pages, 5 figures (Have been accepted by ICME 2024)

点击查看摘要

Abstract:Modern image processing tools have made it easy for attackers to crop the region or object of interest in images and paste it into other images. The challenge this cropping-paste attack poses to the watermarking technology is that it breaks the synchronization of the image watermark, introducing multiple superimposed desynchronization distortions, such as rotation, scaling, and translation. However, current watermarking methods can only resist a single type of desynchronization and cannot be applied to protect the object’s copyright under the cropping-paste attack. With the finding that the key to resisting the cropping-paste attack lies in robust features of the object to protect, this paper proposes a self-synchronizing object-aligned watermarking method, called SSyncOA. Specifically, we first constrain the watermarked region to be aligned with the protected object, and then synchronize the watermark’s translation, rotation, and scaling distortions by normalizing the object invariant features, i.e., its centroid, principal orientation, and minimum bounding square, respectively. To make the watermark embedded in the protected object, we introduce the object-aligned watermarking model, which incorporates the real cropping-paste attack into the encoder-noise layer-decoder pipeline and is optimized end-to-end. Besides, we illustrate the effect of different desynchronization distortions on the watermark training, which confirms the necessity of the self-synchronization process. Extensive experiments demonstrate the superiority of our method over other SOTAs.

CV-25-标题: DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization IJCNN2024

链接: https://arxiv.org/abs/2405.03436
作者: Chengxin Zhao, Hefei Ling, Sijing Xie, Nan Sun, Zongyi Li, Yuxuan Shi, Jiazhong Chen
备注: 7 pages, 6 figures (Have been accepted by IJCNN 2024)

点击查看摘要

Abstract:Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the captured photos before decoding. Existing methods that train models to find the invisible embedded region struggle to obtain accurate localization results, leading to degraded decoding accuracy. This limitation is primarily because the CNN network is sensitive to low-frequency signals, while the embedded signal is typically in the high-frequency form. Based on this, this paper proposes a Dual-Branch Dual-Head (DBDH) neural network tailored for the precise localization of invisible embedded regions. Specifically, DBDH uses a low-level texture branch containing 62 high-pass filters to capture the high-frequency signals induced by embedding. A high-level context branch is used to extract discriminative features between the embedded and normal regions. DBDH employs a detection head to directly detect the four vertices of the embedding region. In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training. The segmentation head provides pixel-level supervision for model learning, facilitating better learning of the embedded signals. Based on two state-of-the-art invisible offline-to-online messaging methods, we construct two datasets and augmentation strategies for training and testing localization models. Extensive experiments demonstrate the superior performance of the proposed DBDH over existing methods.

CV-26-标题: Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network

链接: https://arxiv.org/abs/2405.03420
作者: Emil Benedykciuk, Marcin Denkowski, Grzegorz Wójcik
备注:

点击查看摘要

Abstract:This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using Neural Architecture Search (NAS) methods, specifically Differentiable Architecture Search (DARTS). We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS, designed to be injected into the skip connections of an existing and already trained U-shaped model. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch. The empirical studies, focusing on medical image segmentation tasks, demonstrate the efficacy of this method. The integration of specialized IAC cells into various configurations of the U-Net model increases segmentation accuracy by almost 2% points on average for the validation dataset and over 3% points for the training dataset. The findings of this study not only offer a cost-effective alternative to the complete overhaul of complex models for performance upgrades but also indicate the potential applicability of our method to other architectures and problem domains.

CV-27-标题: Gaussian Splatting: 3D Reconstruction and Novel View Synthesis a Review

链接: https://arxiv.org/abs/2405.03417
作者: Anurag Dalal, Daniel Hagen, Kjell G. Robbersmyr, Kristian Muri Knausgård
备注: 24 pages

点击查看摘要

Abstract:Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have gained attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art techniques for 3D reconstruction, including the generation of novel, unseen views. An overview of recent developments in the Gaussian Splatting method is provided, covering input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this domain and the numerous opportunities for enhancing 3D reconstruction methods, a comprehensive examination of algorithms appears essential. Consequently, this study offers a thorough overview of the latest advancements in Gaussian Splatting.

CV-28-标题: 3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation CVPR2024

链接: https://arxiv.org/abs/2405.03388
作者: Xingguang Zhong, Yue Pan, Cyrill Stachniss, Jens Behley
备注: 10 pages, CVPR 2024

点击查看摘要

Abstract:Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans. To this end, we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation, we extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids, a globally shared decoder, and time-dependent basis functions, which we jointly optimize in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans, we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete 3D maps, outperforming several state-of-the-art methods. Codes are available at: this https URL

CV-29-标题: Statistical Edge Detection And UDF Learning For Shape Representation

链接: https://arxiv.org/abs/2405.03381
作者: Virgile Foy (IMT), Fabrice Gamboa (IMT), Reda Chhaibi (IMT)
备注:

点击查看摘要

Abstract:In the field of computer vision, the numerical encoding of 3D surfaces is crucial. It is classical to represent surfaces with their Signed Distance Functions (SDFs) or Unsigned Distance Functions (UDFs). For tasks like representation learning, surface classification, or surface reconstruction, this function can be learned by a neural network, called Neural Distance Function. This network, and in particular its weights, may serve as a parametric and implicit representation for the surface. The network must represent the surface as accurately as possible. In this paper, we propose a method for learning UDFs that improves the fidelity of the obtained Neural UDF to the original 3D surface. The key idea of our method is to concentrate the learning effort of the Neural UDF on surface edges. More precisely, we show that sampling more training points around surface edges allows better local accuracy of the trained Neural UDF, and thus improves the global expressiveness of the Neural UDF in terms of Hausdorff distance. To detect surface edges, we propose a new statistical method based on the calculation of a p -value at each point on the surface. Our method is shown to detect surface edges more accurately than a commonly used local geometric descriptor.

CV-30-标题: CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer

链接: https://arxiv.org/abs/2405.03376
作者: Tao Han, zhenghao Chen, Song Guo, Wanghan Xu, Lei Bai
备注: Main text and supplementary, 22 pages

点击查看摘要

Abstract:The advent of data-driven weather forecasting models, which learn from hundreds of terabytes (TB) of reanalysis data, has significantly advanced forecasting capabilities. However, the substantial costs associated with data storage and transmission present a major challenge for data providers and users, affecting resource-constrained researchers and limiting their accessibility to participate in AI-based meteorological research. To mitigate this issue, we introduce an efficient neural codec, the Variational Autoencoder Transformer (VAEformer), for extreme compression of climate data to significantly reduce data storage cost, making AI-based meteorological research portable to researchers. Our approach diverges from recent complex neural codecs by utilizing a low-complexity Auto-Encoder transformer. This encoder produces a quantized latent representation through variance inference, which reparameterizes the latent space as a Gaussian distribution. This method improves the estimation of distributions for cross-entropy coding. Extensive experiments demonstrate that our VAEformer outperforms existing state-of-the-art compression methods in the context of climate data. By applying our VAEformer, we compressed the most popular ERA5 climate dataset (226 TB) into a new dataset, CRA5 (0.7 TB). This translates to a compression ratio of over 300 while retaining the dataset’s utility for accurate scientific analysis. Further, downstream experiments show that global weather forecasting models trained on the compact CRA5 dataset achieve forecasting accuracy comparable to the model trained on the original dataset. Code, the CRA5 dataset, and the pre-trained model are available at this https URL.

CV-31-标题: Knowledge-aware Text-Image Retrieval for Remote Sensing Images

链接: https://arxiv.org/abs/2405.03373
作者: Li Mi, Xianjie Dai, Javiera Castillo-Navarro, Devis Tuia
备注: Under review

点击查看摘要

Abstract:Image-based retrieval in large Earth observation archives is challenging because one needs to navigate across thousands of candidate matches only with the query image as a guide. By using text as information supporting the visual query, the retrieval system gains in usability, but at the same time faces difficulties due to the diversity of visual signals that cannot be summarized by a short caption only. For this reason, as a matching-based task, cross-modal text-image retrieval often suffers from information asymmetry between texts and images. To address this challenge, we propose a Knowledge-aware Text-Image Retrieval (KTIR) method for remote sensing images. By mining relevant information from an external knowledge graph, KTIR enriches the text scope available in the search query and alleviates the information gaps between texts and images for better matching. Moreover, by integrating domain-specific knowledge, KTIR also enhances the adaptation of pre-trained vision-language models to remote sensing applications. Experimental results on three commonly used remote sensing text-image retrieval benchmarks show that the proposed knowledge-aware method leads to varied and consistent retrievals, outperforming state-of-the-art retrieval methods.

CV-32-标题: On the Theory of Cross-Modality Distillation with Contrastive Learning

链接: https://arxiv.org/abs/2405.03355
作者: Hangyu Lin, Chen Liu, Chengming Xu, Zhengqi Gao, Yanwei Fu, Yuan Yao
备注:

点击查看摘要

Abstract:Cross-modality distillation arises as an important topic for data modalities containing limited knowledge such as depth maps and high-quality sketches. Such techniques are of great importance, especially for memory and privacy-restricted scenarios where labeled training data is generally unavailable. To solve the problem, existing label-free methods leverage a few pairwise unlabeled data to distill the knowledge by aligning features or statistics between the source and target modalities. For instance, one typically aims to minimize the L2 distance or contrastive loss between the learned features of pairs of samples in the source (e.g. image) and the target (e.g. sketch) modalities. However, most algorithms in this domain only focus on the experimental results but lack theoretical insight. To bridge the gap between the theory and practical method of cross-modality distillation, we first formulate a general framework of cross-modality contrastive distillation (CMCD), built upon contrastive learning that leverages both positive and negative correspondence, towards a better distillation of generalizable features. Furthermore, we establish a thorough convergence analysis that reveals that the distance between source and target modalities significantly impacts the test error on downstream tasks within the target modality which is also validated by the empirical results. Extensive experimental results show that our algorithm outperforms existing algorithms consistently by a margin of 2-3% across diverse modalities and tasks, covering modalities of image, sketch, depth map, and audio and tasks of recognition and segmentation.

CV-33-标题: Salient Object Detection From Arbitrary Modalities

链接: https://arxiv.org/abs/2405.03352
作者: Nianchang Huang, Yang Yang, Ruida Xi, Qiang Zhang, Jungong Han, Jin Huang
备注: 15 Pages, 7 Figures, 8 Tables

点击查看摘要

Abstract:Toward desirable saliency prediction, the types and numbers of inputs for a salient object detection (SOD) algorithm may dynamically change in many real-life applications. However, existing SOD algorithms are mainly designed or trained for one particular type of inputs, failing to be generalized to other types of inputs. Consequentially, more types of SOD algorithms need to be prepared in advance for handling different types of inputs, raising huge hardware and research costs. Differently, in this paper, we propose a new type of SOD task, termed Arbitrary Modality SOD (AM SOD). The most prominent characteristics of AM SOD are that the modality types and modality numbers will be arbitrary or dynamically changed. The former means that the inputs to the AM SOD algorithm may be arbitrary modalities such as RGB, depths, or even any combination of them. While, the latter indicates that the inputs may have arbitrary modality numbers as the input type is changed, e.g. single-modality RGB image, dual-modality RGB-Depth (RGB-D) images or triple-modality RGB-Depth-Thermal (RGB-D-T) images. Accordingly, a preliminary solution to the above challenges, ı.e. a modality switch network (MSN), is proposed in this paper. In particular, a modality switch feature extractor (MSFE) is first designed to extract discriminative features from each modality effectively by introducing some modality indicators, which will generate some weights for modality switching. Subsequently, a dynamic fusion module (DFM) is proposed to adaptively fuse features from a variable number of modalities based on a novel Transformer structure. Finally, a new dataset, named AM-XD, is constructed to facilitate research on AM SOD. Extensive experiments demonstrate that our AM SOD method can effectively cope with changes in the type and number of input modalities for robust salient object detection.

CV-34-标题: Modality Prompt s for Arbitrary Modality Salient Object Detection

链接: https://arxiv.org/abs/2405.03351
作者: Nianchang Huang, Yang Yang, Qiang Zhang, Jungong Han, Jin Huang
备注: 13 pages, 7 Figures, 3 Tables

点击查看摘要

Abstract:This paper delves into the task of arbitrary modality salient object detection (AM SOD), aiming to detect salient objects from arbitrary modalities, eg RGB images, RGB-D images, and RGB-D-T images. A novel modality-adaptive Transformer (MAT) will be proposed to investigate two fundamental challenges of AM SOD, ie more diverse modality discrepancies caused by varying modality types that need to be processed, and dynamic fusion design caused by an uncertain number of modalities present in the inputs of multimodal fusion strategy. Specifically, inspired by prompt learning’s ability of aligning the distributions of pre-trained models to the characteristic of downstream tasks by learning some prompts, MAT will first present a modality-adaptive feature extractor (MAFE) to tackle the diverse modality discrepancies by introducing a modality prompt for each modality. In the training stage, a new modality translation contractive (MTC) loss will be further designed to assist MAFE in learning those modality-distinguishable modality prompts. Accordingly, in the testing stage, MAFE can employ those learned modality prompts to adaptively adjust its feature space according to the characteristics of the input modalities, thus being able to extract discriminative unimodal features. Then, MAFE will present a channel-wise and spatial-wise fusion hybrid (CSFH) strategy to meet the demand for dynamic fusion. For that, CSFH dedicates a channel-wise dynamic fusion module (CDFM) and a novel spatial-wise dynamic fusion module (SDFM) to fuse the unimodal features from varying numbers of modalities and meanwhile effectively capture cross-modal complementary semantic and detail information, respectively. Moreover, CSFH will carefully align CDFM and SDFM to different levels of unimodal features based on their characteristics for more effective complementary information exploitation.

CV-35-标题: Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement

链接: https://arxiv.org/abs/2405.03349
作者: Jiesong Bai, Yuhao Yin, Qiyuan He
备注:

点击查看摘要

Abstract:In the field of low-light image enhancement, both traditional Retinex methods and advanced deep learning techniques such as Retinexformer have shown distinct advantages and limitations. Traditional Retinex methods, designed to mimic the human eye’s perception of brightness and color, decompose images into illumination and reflection components but struggle with noise management and detail preservation under low light conditions. Retinexformer enhances illumination estimation through traditional self-attention mechanisms, but faces challenges with insufficient interpretability and suboptimal enhancement effects. To overcome these limitations, this paper introduces the RetinexMamba architecture. RetinexMamba not only captures the physical intuitiveness of traditional Retinex methods but also integrates the deep learning framework of Retinexformer, leveraging the computational efficiency of State Space Models (SSMs) to enhance processing speed. This architecture features innovative illumination estimators and damage restorer mechanisms that maintain image quality during enhancement. Moreover, RetinexMamba replaces the IG-MSA (Illumination-Guided Multi-Head Attention) in Retinexformer with a Fused-Attention mechanism, improving the model’s interpretability. Experimental evaluations on the LOL dataset show that RetinexMamba outperforms existing deep learning approaches based on Retinex theory in both quantitative and qualitative metrics, confirming its effectiveness and superiority in enhancing low-light images.

CV-36-标题: Light-VQA: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance

链接: https://arxiv.org/abs/2405.03333
作者: Xunchu Zhou, Xiaohong Liu, Yunlong Dong, Tengchuan Kou, Yixuan Gao, Zicheng Zhang, Chunyi Li, Haoning Wu, Guangtao Zhai
备注:

点击查看摘要

Abstract:Recently, User-Generated Content (UGC) videos have gained popularity in our daily lives. However, UGC videos often suffer from poor exposure due to the limitations of photographic equipment and techniques. Therefore, Video Exposure Correction (VEC) algorithms have been proposed, Low-Light Video Enhancement (LLVE) and Over-Exposed Video Recovery (OEVR) included. Equally important to the VEC is the Video Quality Assessment (VQA). Unfortunately, almost all existing VQA models are built generally, measuring the quality of a video from a comprehensive perspective. As a result, Light-VQA, trained on LLVE-QA, is proposed for assessing LLVE. We extend the work of Light-VQA by expanding the LLVE-QA dataset into Video Exposure Correction Quality Assessment (VEC-QA) dataset with over-exposed videos and their corresponding corrected versions. In addition, we propose Light-VQA+, a VQA model specialized in assessing VEC. Light-VQA+ differs from Light-VQA mainly from the usage of the CLIP model and the vision-language guidance during the feature extraction, followed by a new module referring to the Human Visual System (HVS) for more accurate assessment. Extensive experimental results show that our model achieves the best performance against the current State-Of-The-Art (SOTA) VQA models on the VEC-QA dataset and other public datasets.

CV-37-标题: Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge

链接: https://arxiv.org/abs/2405.03328
作者: Lemuel Puglisi, Daniel C. Alexander, Daniele Ravì
备注:

点击查看摘要

Abstract:In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer’s Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: this https URL.

CV-38-标题: Enhancing DETRs Variants through Improved Content Query and Similar Query Aggregation

链接: https://arxiv.org/abs/2405.03318
作者: Yingying Zhang, Chuangji Shi, Xin Guo, Jiangwei Lao, Jian Wang, Jiaotuan Wang, Jingdong Chen
备注: 11 pages, 7 figures

点击查看摘要

Abstract:The design of the query is crucial for the performance of DETR and its variants. Each query consists of two components: a content part and a positional one. Traditionally, the content query is initialized with a zero or learnable embedding, lacking essential content information and resulting in sub-optimal performance. In this paper, we introduce a novel plug-and-play module, Self-Adaptive Content Query (SACQ), to address this limitation. The SACQ module utilizes features from the transformer encoder to generate content queries via self-attention pooling. This allows candidate queries to adapt to the input image, resulting in a more comprehensive content prior and better focus on target objects. However, this improved concentration poses a challenge for the training process that utilizes the Hungarian matching, which selects only a single candidate and suppresses other similar ones. To overcome this, we propose a query aggregation strategy to cooperate with SACQ. It merges similar predicted candidates from different queries, easing the optimization. Our extensive experiments on the COCO dataset demonstrate the effectiveness of our proposed approaches across six different DETR’s variants with multiple configurations, achieving an average improvement of over 1.0 AP.

CV-39-标题: Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery

链接: https://arxiv.org/abs/2405.03314
作者: Maximilian Weber, Daniel Wild, Jens Kleesiek, Jan Egger, Christina Gsaxner
备注: 5 pages, 4 figures; accepted at IEEE ISBI 2024

点击查看摘要

Abstract:Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point cloud registration. The main objective is to evaluate the feasibility of applying deep learning-based point cloud registration methods for image-to-patient registration in augmented reality-guided surgery. We created a dataset of point clouds from medical imaging and corresponding point clouds captured with a popular AR device, the HoloLens 2. We evaluate three well-established deep learning models in registering these data pairs. While we find that some deep learning methods show promise, we show that a conventional registration pipeline still outperforms them on our challenging dataset.

CV-40-标题: Federated Learning for Drowsiness Detection in Connected Vehicles

链接: https://arxiv.org/abs/2405.03311
作者: William Lindskog, Valentin Spannagl, Christian Prehofer
备注: 14 pages, 8 figures, 1 table, EAI INTSYS 2023 conference

点击查看摘要

Abstract:Ensuring driver readiness poses challenges, yet driver monitoring systems can assist in determining the driver’s state. By observing visual cues, such systems recognize various behaviors and associate them with specific conditions. For instance, yawning or eye blinking can indicate driver drowsiness. Consequently, an abundance of distributed data is generated for driver monitoring. Employing machine learning techniques, such as driver drowsiness detection, presents a potential solution. However, transmitting the data to a central machine for model training is impractical due to the large data size and privacy concerns. Conversely, training on a single vehicle would limit the available data and likely result in inferior performance. To address these issues, we propose a federated learning framework for drowsiness detection within a vehicular network, leveraging the YawDD dataset. Our approach achieves an accuracy of 99.2%, demonstrating its promise and comparability to conventional deep learning techniques. Lastly, we show how our model scales using various number of federated clients

CV-41-标题: Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification

链接: https://arxiv.org/abs/2405.03301
作者: Matteo Bianchi, Antonio De Santis, Andrea Tocchetti, Marco Brambilla
备注: International Joint Conference on Artificial Intelligence 2024 (to be published)

点击查看摘要

Abstract:Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainability methods generate saliency maps to show where a specific class is identified, without providing a detailed explanation of the model’s decision process. Striving to address such a need, we introduce a post-hoc method that explains the entire feature extraction process of a Convolutional Neural Network. These explanations include a layer-wise representation of the features the model extracts from the input. Such features are represented as saliency maps generated by clustering and merging similar feature maps, to which we associate a weight derived by generalizing Grad-CAM for the proposed methodology. To further enhance these explanations, we include a set of textual labels collected through a gamified crowdsourcing activity and processed using NLP techniques and Sentence-BERT. Finally, we show an approach to generate global explanations by aggregating labels across multiple images.

CV-42-标题: Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity

链接: https://arxiv.org/abs/2405.03280
作者: Yizhuo Lu, Changde Du, Chong Wang, Xuanliu Zhu, Liuyun Jiang, Huiguang He
备注:

点击查看摘要

Abstract:Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. The difficulty stems from two primary issues: (1) vision-processing mechanisms in the brain are highly intricate and not fully revealed, making it challenging to directly learn a mapping between fMRI and video; (2) the temporal resolution of fMRI is significantly lower than that of natural videos. To overcome these issues, this paper propose a two-stage model named Mind-Animator, which achieves state-of-the-art performance on three public datasets. Specifically, during the fMRI-to-feature stage, we decouple semantic, structural, and motion features from fMRI through fMRI-vision-language tri-modal contrastive learning and sparse causal attention. In the feature-to-video stage, these features are merged to videos by an inflated Stable Diffusion. We substantiate that the reconstructed video dynamics are indeed derived from fMRI, rather than hallucinations of the generative model, through permutation tests. Additionally, the visualization of voxel-wise and ROI-wise importance maps confirms the neurobiological interpretability of our model.

CV-43-标题: WorldQA: Multimodal World Knowledge in Videos through Long-Chain Reasoning

链接: https://arxiv.org/abs/2405.03272
作者: Yuanhan Zhang, Kaichen Zhang, Bo Li, Fanyi Pu, Christopher Arif Setiadharma, Jingkang Yang, Ziwei Liu
备注:

点击查看摘要

Abstract:Multimodal information, together with our knowledge, help us to understand the complex and dynamic world. Large language models (LLM) and large multimodal models (LMM), however, still struggle to emulate this capability. In this paper, we present WorldQA, a video understanding dataset designed to push the boundaries of multimodal world models with three appealing properties: (1) Multimodal Inputs: The dataset comprises 1007 question-answer pairs and 303 videos, necessitating the analysis of both auditory and visual data for successful interpretation. (2) World Knowledge: We identify five essential types of world knowledge for question formulation. This approach challenges models to extend their capabilities beyond mere perception. (3) Long-Chain Reasoning: Our dataset introduces an average reasoning step of 4.45, notably surpassing other videoQA datasets. Furthermore, we introduce WorldRetriever, an agent designed to synthesize expert knowledge into a coherent reasoning chain, thereby facilitating accurate responses to WorldQA queries. Extensive evaluations of 13 prominent LLMs and LMMs reveal that WorldRetriever, although being the most effective model, achieved only 70% of humanlevel performance in multiple-choice questions. This finding highlights the necessity for further advancement in the reasoning and comprehension abilities of models. Our experiments also yield several key insights. For instance, while humans tend to perform better with increased frames, current LMMs, including WorldRetriever, show diminished performance under similar conditions. We hope that WorldQA,our methodology, and these insights could contribute to the future development of multimodal world models.

CV-44-标题: Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data

链接: https://arxiv.org/abs/2405.03243
作者: Leonhard Hennicke, Christian Medeiros Adriano, Holger Giese, Jan Mathias Koehler, Lukas Schott
备注:

点击查看摘要

Abstract:Generative foundation models like Stable Diffusion comprise a diverse spectrum of knowledge in computer vision with the potential for transfer learning, e.g., via generating data to train student models for downstream tasks. This could circumvent the necessity of collecting labeled real-world data, thereby presenting a form of data-free knowledge distillation. However, the resultant student models show a significant drop in accuracy compared to models trained on real data. We investigate possible causes for this drop and focus on the role of the different layers of the student model. By training these layers using either real or synthetic data, we reveal that the drop mainly stems from the model’s final layers. Further, we briefly investigate other factors, such as differences in data-normalization between synthetic and real, the impact of data augmentations, texture vs.\ shape learning, and assuming oracle prompts. While we find that some of those factors can have an impact, they are not sufficient to close the gap towards real data. Building upon our insights that mainly later layers are responsible for the drop, we investigate the data-efficiency of fine-tuning a synthetically trained model with real data applied to only those last layers. Our results suggest an improved trade-off between the amount of real training data used and the model’s accuracy. Our findings contribute to the understanding of the gap between synthetic and real data and indicate solutions to mitigate the scarcity of labeled real data.

CV-45-标题: Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks

链接: https://arxiv.org/abs/2405.03235
作者: Xuran Zhu
备注:

点击查看摘要

Abstract:Brain disorders are a major challenge to global health, causing millions of deaths each year. Accurate diagnosis of these diseases relies heavily on advanced medical imaging techniques such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, the scarcity of annotated data poses a significant challenge in deploying machine learning models for medical diagnosis. To address this limitation, deep learning techniques have shown considerable promise. Domain adaptation techniques enhance a model’s ability to generalize across imaging modalities by transferring knowledge from one domain (e.g., CT images) to another (e.g., MRI images). Such cross-modality adaptation is essential to improve the ability of models to consistently generalize across different imaging modalities. This study collected relevant resources from the Kaggle website and employed the Maximum Mean Difference (MMD) method - a popular domain adaptation method - to reduce the differences between imaging domains. By combining MMD with Convolutional Neural Networks (CNNs), the accuracy and utility of the model is obviously enhanced. The excellent experimental results highlight the great potential of data-driven domain adaptation techniques to improve diagnostic accuracy and efficiency, especially in resource-limited environments. By bridging the gap between different imaging modalities, the study aims to provide clinicians with more reliable diagnostic tools.

CV-46-标题: Spatial and Surface Correspondence Field for Interaction Transfer SIGGRAPH2024

链接: https://arxiv.org/abs/2405.03221
作者: Zeyu Huang, Honghao Xu, Haibin Huang, Chongyang Ma, Hui Huang, Ruizhen Hu
备注: Accepted to SIGGRAPH 2024, project page at this https URL

点击查看摘要

Abstract:In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.

CV-47-标题: Elevator Escalator or Neither? Classifying Pedestrian Conveyor State Using Inertial Navigation System

链接: https://arxiv.org/abs/2405.03218
作者: Tianlang He, Zhiqiu Xia, S.-H. Gary Chan
备注:

点击查看摘要

Abstract:Classifying a pedestrian in one of the three conveyor states of “elevator,” “escalator” and “neither” is fundamental to many applications such as indoor localization and people flow analysis. We estimate, for the first time, the pedestrian conveyor state given the inertial navigation system (INS) readings of accelerometer, gyroscope and magnetometer sampled from the phone. Our problem is challenging because the INS signals of the conveyor state are coupled and perturbed by unpredictable arbitrary human actions, confusing the decision process. We propose ELESON, a novel, effective and lightweight INS-based deep learning approach to classify whether a pedestrian is in an elevator, escalator or neither. ELESON utilizes a motion feature extractor to decouple the conveyor state from human action in the feature space, and a magnetic feature extractor to account for the speed difference between elevator and escalator. Given the results of the extractors, it employs an evidential state classifier to estimate the confidence of the pedestrian states. Based on extensive experiments conducted on twenty hours of real pedestrian data, we demonstrate that ELESON outperforms significantly the state-of-the-art approaches (where combined INS signals of both the conveyor state and human actions are processed together), with 15% classification improvement in F1 score, stronger confidence discriminability with 10% increase in AUROC (Area Under the Receiver Operating Characteristics), and low computational and memory requirements on smartphones.

CV-48-标题: Hierarchical Space-Time Attention for Micro-Expression Recognition

链接: https://arxiv.org/abs/2405.03202
作者: Haihong Hao, Shuo Wang, Huixia Ben, Yanbin Hao, Yansong Wang, Weiwei Wang
备注: 9 pages, 4 figures

点击查看摘要

Abstract:Micro-expression recognition (MER) aims to recognize the short and subtle facial movements from the Micro-expression (ME) video clips, which reveal real emotions. Recent MER methods mostly only utilize special frames from ME video clips or extract optical flow from these special frames. However, they neglect the relationship between movements and space-time, while facial cues are hidden within these relationships. To solve this issue, we propose the Hierarchical Space-Time Attention (HSTA). Specifically, we first process ME video frames and special frames or data parallelly by our cascaded Unimodal Space-Time Attention (USTA) to establish connections between subtle facial movements and specific facial areas. Then, we design Crossmodal Space-Time Attention (CSTA) to achieve a higher-quality fusion for crossmodal data. Finally, we hierarchically integrate USTA and CSTA to grasp the deeper facial cues. Our model emphasizes temporal modeling without neglecting the processing of special data, and it fuses the contents in different modalities while maintaining their respective uniqueness. Extensive experiments on the four benchmarks show the effectiveness of our proposed HSTA. Specifically, compared with the latest method on the CASME3 dataset, it achieves about 3% score improvement in seven-category classification.

CV-49-标题: StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception

链接: https://arxiv.org/abs/2405.03197
作者: Zhiwei Wang, Xiaoyu Zeng, Chongwei Wu, Jinxin lv, Xu Zhang, Wei Fang, Qiang Li
备注: 9 pages, 8 figures, 2 tables

点击查看摘要

Abstract:One-shot segmentation of brain tissue requires training registration-segmentation (reg-seg) dual-model iteratively, where reg-model aims to provide pseudo masks of unlabeled images for seg-model by warping a carefully-labeled atlas. However, the imperfect reg-model induces image-mask misalignment, poisoning the seg-model subsequently. Recent StyleSeg bypasses this bottleneck by replacing the unlabeled images with their warped copies of atlas, but needs to borrow the diverse image patterns via style transformation. Here, we present StyleSeg V2, inherited from StyleSeg but granted the ability of perceiving the registration errors. The motivation is that good registration behaves in a mirrored fashion for mirrored images. Therefore, almost at no cost, StyleSeg V2 can have reg-model itself “speak out” incorrectly-aligned regions by simply mirroring (symmetrically flipping the brain) its input, and the registration errors are symmetric inconsistencies between the outputs of original and mirrored inputs. Consequently, StyleSeg V2 allows the seg-model to make use of correctly-aligned regions of unlabeled images and also enhances the fidelity of style-transformed warped atlas image by weighting the local transformation strength according to registration errors. The experimental results on three public datasets demonstrate that our proposed StyleSeg V2 outperforms other state-of-the-arts by considerable margins, and exceeds StyleSeg by increasing the average Dice by at least 2.4%.

CV-50-标题: CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario CVPR2024

链接: https://arxiv.org/abs/2405.03194
作者: Zhizhao Duan, Hao Cheng, Duo Xu, Xi Wu, Xiangxie Zhang, Xi Ye, Zhen Xie
备注: Accepted by AICITY2024 Workshop Track2 at CVPR2024

点击查看摘要

Abstract:In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention. This paper introduces CityLLaVA, a novel fine-tuning framework for Visual Language Models (VLMs) designed for urban scenarios. CityLLaVA enhances model comprehension and prediction accuracy through (1) employing bounding boxes for optimal visual data preprocessing, including video best-view selection and visual prompt engineering during both training and testing phases; (2) constructing concise Question-Answer sequences and designing textual prompts to refine instruction comprehension; (3) implementing block expansion to fine-tune large VLMs efficiently; and (4) advancing prediction accuracy via a unique sequential questioning-based prediction augmentation. Demonstrating top-tier performance, our method achieved a benchmark score of 33.4308, securing the leading position on the leaderboard. The code can be found: this https URL

CV-51-标题: Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability

链接: https://arxiv.org/abs/2405.03193
作者: Juanjuan Weng, Zhiming Luo, Shaozi Li
备注:

点击查看摘要

Abstract:Recent studies have shown that Deep Neural Networks (DNNs) are susceptible to adversarial attacks, with frequency-domain analysis underscoring the significance of high-frequency components in influencing model predictions. Conversely, targeting low-frequency components has been effective in enhancing attack transferability on black-box models. In this study, we introduce a frequency decomposition-based feature mixing method to exploit these frequency characteristics in both clean and adversarial samples. Our findings suggest that incorporating features of clean samples into adversarial features extracted from adversarial examples is more effective in attacking normally-trained models, while combining clean features with the adversarial features extracted from low-frequency parts decomposed from the adversarial samples yields better results in attacking defense models. However, a conflict issue arises when these two mixing approaches are employed simultaneously. To tackle the issue, we propose a cross-frequency meta-optimization approach comprising the meta-train step, meta-test step, and final update. In the meta-train step, we leverage the low-frequency components of adversarial samples to boost the transferability of attacks against defense models. Meanwhile, in the meta-test step, we utilize adversarial samples to stabilize gradients, thereby enhancing the attack’s transferability against normally trained models. For the final update, we update the adversarial sample based on the gradients obtained from both meta-train and meta-test steps. Our proposed method is evaluated through extensive experiments on the ImageNet-Compatible dataset, affirming its effectiveness in improving the transferability of attacks on both normally-trained CNNs and defense models. The source code is available at this https URL.

CV-52-标题: Adapting Dual-encoder Vision-language Models for Paraphrased Retrieval

链接: https://arxiv.org/abs/2405.03190
作者: Jiacheng Cheng, Hijung Valentina Shin, Nuno Vasconcelos, Bryan Russell, Fabian Caba Heilbron
备注:

点击查看摘要

Abstract:In the recent years, the dual-encoder vision-language models (\eg CLIP) have achieved remarkable text-to-image retrieval performance. However, we discover that these models usually results in very different retrievals for a pair of paraphrased queries. Such behavior might render the retrieval system less predictable and lead to user frustration. In this work, we consider the task of paraphrased text-to-image retrieval where a model aims to return similar results given a pair of paraphrased queries. To start with, we collect a dataset of paraphrased image descriptions to facilitate quantitative evaluation for this task. We then hypothesize that the undesired behavior of existing dual-encoder model is due to their text towers which are trained on image-sentence pairs and lack the ability to capture the semantic similarity between paraphrased queries. To improve on this, we investigate multiple strategies for training a dual-encoder model starting from a language model pretrained on a large text corpus. Compared to public dual-encoder models such as CLIP and OpenCLIP, the model trained with our best adaptation strategy achieves a significantly higher ranking similarity for paraphrased queries while maintaining similar zero-shot classification and retrieval accuracy.

CV-53-标题: Transformer -based RGB-T Tracking with Channel and Spatial Feature Fusion

链接: https://arxiv.org/abs/2405.03177
作者: Yunfeng Li, Bo Wang, Ye Li, Zhiwen Yu, Liang Wang
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

点击查看摘要

Abstract:Complementary RGB and TIR modalities enable RGB-T tracking to achieve competitive performance in challenging scenarios. Therefore, how to better fuse cross-modal features is the core issue of RGB-T tracking. Some previous methods either insufficiently fuse RGB and TIR features, or depend on intermediaries containing information from both modalities to achieve cross-modal information interaction. The former does not fully exploit the potential of using only RGB and TIR information of the template or search region for channel and spatial feature fusion, and the latter lacks direct interaction between the template and search area, which limits the model’s ability to fully exploit the original semantic information of both modalities. To alleviate these limitations, we explore how to improve the performance of a visual Transformer by using direct fusion of cross-modal channels and spatial features, and propose CSTNet. CSTNet uses ViT as a backbone and inserts cross-modal channel feature fusion modules (CFM) and cross-modal spatial feature fusion modules (SFM) for direct interaction between RGB and TIR features. The CFM performs parallel joint channel enhancement and joint multilevel spatial feature modeling of RGB and TIR features and sums the features, and then globally integrates the sum feature with the original features. The SFM uses cross-attention to model the spatial relationship of cross-modal features and then introduces a convolutional feedforward network for joint spatial and channel integration of multimodal features. Comprehensive experiments show that CSTNet achieves state-of-the-art performance on three public RGB-T tracking benchmarks. Code is available at this https URL.

CV-54-标题: The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning

链接: https://arxiv.org/abs/2405.03164
作者: Ransalu Senanayake
备注:

点击查看摘要

Abstract:Uncertainty has long been a critical area of study in robotics, particularly when robots are equipped with analytical models. As we move towards the widespread use of deep neural networks in robots, which have demonstrated remarkable performance in research settings, understanding the nuances of uncertainty becomes crucial for their real-world deployment. This guide offers an overview of the importance of uncertainty and provides methods to quantify and evaluate it from an applications perspective.

CV-55-标题: DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging

链接: https://arxiv.org/abs/2405.03159
作者: Wenxin Fan, Jian Cheng, Cheng Li, Xinrui Ma, Jing Yang, Juan Zou, Ruoyou Wu, Zan Chen, Yuanjing Feng, Hairong Zheng, Shanshan Wang
备注:

点击查看摘要

Abstract:Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep understanding of the brain microstructures. However, the efficiency and accuracy in the multi-parametric estimations are still limited since previous studies tend to estimate multi-parametric maps with dense sampling and isolated signal modeling. This paper proposes DeepMpMRI, a unified framework for fast and high-fidelity multi-parametric estimation from various diffusion models using sparsely sampled q-space data. DeepMpMRI is equipped with a newly designed tensor-decomposition-based regularizer to effectively capture fine details by exploiting the correlation across parameters. In addition, we introduce a Nesterov-based adaptive learning algorithm that optimizes the regularization parameter dynamically to enhance the performance. DeepMpMRI is an extendable framework capable of incorporating flexible network architecture. Experimental results demonstrate the superiority of our approach over 5 state-of-the-art methods in simultaneously estimating multi-parametric maps for various diffusion models with fine-grained details both quantitatively and qualitatively, achieving 4.5 - 22.5 \times acceleration compared to the dense sampling of a total of 270 diffusion gradients.

CV-56-标题: Video Diffusion Models: A Survey

链接: https://arxiv.org/abs/2405.03150
作者: Andrew Melnik, Michal Ljubljanac, Cong Lu, Qi Yan, Weiming Ren, Helge Ritter
备注:

点击查看摘要

Abstract:Diffusion generative models have recently become a robust technique for producing and modifying coherent, high-quality video. This survey offers a systematic overview of critical elements of diffusion models for video generation, covering applications, architectural choices, and the modeling of temporal dynamics. Recent advancements in the field are summarized and grouped into development trends. The survey concludes with an overview of remaining challenges and an outlook on the future of the field. Website: this https URL

CV-57-标题: PTQ4SAM: Post-Training Quantization for Segment Anything CVPR2024

链接: https://arxiv.org/abs/2405.03144
作者: Chengtao Lv, Hong Chen, Jinyang Guo, Yifu Ding, Xianglong Liu
备注: CVPR 2024

点击查看摘要

Abstract:Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks. However, as a large-scale model, the immense memory and computation costs hinder its practical deployment. In this paper, we propose a post-training quantization (PTQ) framework for Segment Anything Model, namely PTQ4SAM. First, we investigate the inherent bottleneck of SAM quantization attributed to the bimodal distribution in post-Key-Linear activations. We analyze its characteristics from both per-tensor and per-channel perspectives, and propose a Bimodal Integration strategy, which utilizes a mathematically equivalent sign operation to transform the bimodal distribution into a relatively easy-quantized normal distribution offline. Second, SAM encompasses diverse attention mechanisms (i.e., self-attention and two-way cross-attention), resulting in substantial variations in the post-Softmax distributions. Therefore, we introduce an Adaptive Granularity Quantization for Softmax through searching the optimal power-of-two base, which is hardware-friendly. Extensive experimental results across various vision tasks (instance segmentation, semantic segmentation and object detection), datasets and model variants show the superiority of PTQ4SAM. For example, when quantizing SAM-L to 6-bit, we achieve lossless accuracy for instance segmentation, about 0.5% drop with theoretical 3.9 \times acceleration. The code is available at \urlthis https URL.

CV-58-标题: AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding

链接: https://arxiv.org/abs/2405.03121
作者: Tao Liu, Feilong Chen, Shuai Fan, Chenpeng Du, Qi Chen, Xie Chen, Kai Yu
备注: 14 pages, 7 figures

点击查看摘要

Abstract:The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait. Unlike existing models that primarily focus on verbal cues such as lip synchronization and fail to capture the complex dynamics of facial expressions and nonverbal cues, AniTalker employs a universal motion representation. This innovative representation effectively captures a wide range of facial dynamics, including subtle expressions and head movements. AniTalker enhances motion depiction through two self-supervised learning strategies: the first involves reconstructing target video frames from source frames within the same identity to learn subtle motion representations, and the second develops an identity encoder using metric learning while actively minimizing mutual information between the identity and motion encoders. This approach ensures that the motion representation is dynamic and devoid of identity-specific details, significantly reducing the need for labeled data. Additionally, the integration of a diffusion model with a variance adapter allows for the generation of diverse and controllable facial animations. This method not only demonstrates AniTalker’s capability to create detailed and realistic facial movements but also underscores its potential in crafting dynamic avatars for real-world applications. Synthetic results can be viewed at this https URL.

CV-59-标题: Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning

链接: https://arxiv.org/abs/2405.03109
作者: Weihao Jiang, Chang Liu, Kun He
备注:

点击查看摘要

Abstract:Humans possess remarkable ability to accurately classify new, unseen images after being exposed to only a few examples. Such ability stems from their capacity to identify common features shared between new and previously seen images while disregarding distractions such as background variations. However, for artificial neural network models, determining the most relevant features for distinguishing between two images with limited samples presents a challenge. In this paper, we propose an intra-task mutual attention method for few-shot learning, that involves splitting the support and query samples into patches and encoding them using the pre-trained Vision Transformer (ViT) architecture. Specifically, we swap the class (CLS) token and patch tokens between the support and query sets to have the mutual attention, which enables each set to focus on the most useful information. This facilitates the strengthening of intra-class representations and promotes closer proximity between instances of the same class. For implementation, we adopt the ViT-based network architecture and utilize pre-trained model parameters obtained through self-supervision. By leveraging Masked Image Modeling as a self-supervised training task for pre-training, the pre-trained model yields semantically meaningful representations while successfully avoiding supervision collapse. We then employ a meta-learning method to fine-tune the last several layers and CLS token modules. Our strategy significantly reduces the num- ber of parameters that require fine-tuning while effectively uti- lizing the capability of pre-trained model. Extensive experiments show that our framework is simple, effective and computationally efficient, achieving superior performance as compared to the state-of-the-art baselines on five popular few-shot classification benchmarks under the 5-shot and 1-shot scenarios

CV-60-标题: GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding ICDAR2024

链接: https://arxiv.org/abs/2405.03104
作者: Nil Biescas, Carlos Boned, Josep Lladós, Sanket Biswas
备注: Accepted in ICDAR 2024 (Athens, Greece)

点击查看摘要

Abstract:This paper presents GeoContrastNet, a language-agnostic framework to structured document understanding (DU) by integrating a contrastive learning objective with graph attention networks (GATs), emphasizing the significant role of geometric features. We propose a novel methodology that combines geometric edge features with visual features within an overall two-staged GAT-based framework, demonstrating promising results in both link prediction and semantic entity recognition performance. Our findings reveal that combining both geometric and visual features could match the capabilities of large DU models that rely heavily on Optical Character Recognition (OCR) features in terms of performance accuracy and efficiency. This approach underscores the critical importance of relational layout information between the named text entities in a semi-structured layout of a page. Specifically, our results highlight the model’s proficiency in identifying key-value relationships within the FUNSD dataset for forms and also discovering the spatial relationships in table-structured layouts for RVLCDIP business invoices. Our code and pretrained models will be accessible on our official GitHub.

CV-61-标题: Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLM s ICML2024

链接: https://arxiv.org/abs/2405.03103
作者: Jordan Dotzel, Yuzong Chen, Bahaa Kotb, Sushma Prasad, Gang Wu, Sheng Li, Mohamed S. Abdelfattah, Zhiru Zhang
备注: Accepted to ICML 2024

点击查看摘要

Abstract:Large language models (LLMs) have recently achieved state-of-the-art performance across various tasks, yet due to their large computational requirements, they struggle with strict latency and power demands. Deep neural network (DNN) quantization has traditionally addressed these limitations by converting models to low-precision integer formats. Yet recently alternative formats, such as Normal Float (NF4), have been shown to consistently increase model accuracy, albeit at the cost of increased chip area. In this work, we first conduct a large-scale analysis of LLM weights and activations across 30 networks to conclude most distributions follow a Student’s t-distribution. We then derive a new theoretically optimal format, Student Float (SF4), with respect to this distribution, that improves over NF4 across modern LLMs, for example increasing the average accuracy on LLaMA2-7B by 0.76% across tasks. Using this format as a high-accuracy reference, we then propose augmenting E2M1 with two variants of supernormal support for higher model accuracy. Finally, we explore the quality and performance frontier across 11 datatypes, including non-traditional formats like Additive-Powers-of-Two (APoT), by evaluating their model accuracy and hardware complexity. We discover a Pareto curve composed of INT4, E2M1, and E2M1 with supernormal support, which offers a continuous tradeoff between model accuracy and chip area. For example, E2M1 with supernormal support increases the accuracy of Phi-2 by up to 2.19% with 1.22% area overhead, enabling more LLM-based applications to be run at four bits.

CV-62-标题: Sketch GPT : Autoregressive Modeling for Sketch Generation and Recognition ICDAR2024

链接: https://arxiv.org/abs/2405.03099
作者: Adarsh Tiwari, Sanket Biswas, Josep Lladós
备注: Accepted in ICDAR 2024

点击查看摘要

Abstract:We present SketchGPT, a flexible framework that employs a sequence-to-sequence autoregressive model for sketch generation, and completion, and an interpretation case study for sketch recognition. By mapping complex sketches into simplified sequences of abstract primitives, our approach significantly streamlines the input for autoregressive modeling. SketchGPT leverages the next token prediction objective strategy to understand sketch patterns, facilitating the creation and completion of drawings and also categorizing them accurately. This proposed sketch representation strategy aids in overcoming existing challenges of autoregressive modeling for continuous stroke data, enabling smoother model training and competitive performance. Our findings exhibit SketchGPT’s capability to generate a diverse variety of drawings by adding both qualitative and quantitative comparisons with existing state-of-the-art, along with a comprehensive human evaluation study. The code and pretrained models will be released on our official GitHub.

CV-63-标题: Research on Image Recognition Technology Based on Multimodal Deep Learning

链接: https://arxiv.org/abs/2405.03091
作者: Jinyin Wang, Xingchen Li, Yixuan Jin, Yihao Zhong, Keke Zhang, Chang Zhou
备注:

点击查看摘要

Abstract:This project investigates the human multi-modal behavior identification algorithm utilizing deep neural networks. According to the characteristics of different modal information, different deep neural networks are used to adapt to different modal video information. Through the integration of various deep neural networks, the algorithm successfully identifies behaviors across multiple modalities. In this project, multiple cameras developed by Microsoft Kinect were used to collect corresponding bone point data based on acquiring conventional images. In this way, the motion features in the image can be extracted. Ultimately, the behavioral characteristics discerned through both approaches are synthesized to facilitate the precise identification and categorization of behaviors. The performance of the suggested algorithm was evaluated using the MSR3D data set. The findings from these experiments indicate that the accuracy in recognizing behaviors remains consistently high, suggesting that the algorithm is reliable in various scenarios. Additionally, the tests demonstrate that the algorithm substantially enhances the accuracy of detecting pedestrian behaviors in video footage.

CV-64-标题: Multi-hop graph transformer network for 3D human pose estimation

链接: https://arxiv.org/abs/2405.03055
作者: Zaedul Islam, A. Ben Hamza
备注:

点击查看摘要

Abstract:Accurate 3D human pose estimation is a challenging task due to occlusion and depth ambiguity. In this paper, we introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos by leveraging the strengths of multi-head self-attention and multi-hop graph convolutional networks with disentangled neighborhoods to capture spatio-temporal dependencies and handle long-range interactions. The proposed network architecture consists of a graph attention block composed of stacked layers of multi-head self-attention and graph convolution with learnable adjacency matrix, and a multi-hop graph convolutional block comprised of multi-hop convolutional and dilated convolutional layers. The combination of multi-head self-attention and multi-hop graph convolutional layers enables the model to capture both local and global dependencies, while the integration of dilated convolutional layers enhances the model’s ability to handle spatial details required for accurate localization of the human body joints. Extensive experiments demonstrate the effectiveness and generalization ability of our model, achieving competitive performance on benchmark datasets.

CV-65-标题: Performance Evaluation of Real-Time Object Detection for Electric Scooters

链接: https://arxiv.org/abs/2405.03039
作者: Dong Chen, Arman Hosseini, Arik Smith, Amir Farzin Nikkhah, Arsalan Heydarian, Omid Shoghli, Bradford Campbell
备注: 10 pages, 3 figures

点击查看摘要

Abstract:Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a concerning increase in related injuries and fatalities. Recently, while deep-learning object detection holds paramount significance in autonomous vehicles to avoid potential collisions, its application in the context of e-scooters remains relatively unexplored. This paper addresses this gap by assessing the effectiveness and efficiency of cutting-edge object detectors designed for e-scooters. To achieve this, the first comprehensive benchmark involving 22 state-of-the-art YOLO object detectors, including five versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, and YOLOv8), has been established for real-time traffic object detection using a self-collected dataset featuring e-scooters. The detection accuracy, measured in terms of mAP@0.5, ranges from 27.4% (YOLOv7-E6E) to 86.8% (YOLOv5s). All YOLO models, particularly YOLOv3-tiny, have displayed promising potential for real-time object detection in the context of e-scooters. Both the traffic scene dataset (this https URL) and software program codes (this https URL) for model benchmarking in this study are publicly available, which will not only improve e-scooter safety with advanced object detection but also lay the groundwork for tailored solutions, promising a safer and more sustainable urban micromobility landscape.

CV-66-标题: Matten: Video Generation with Mamba-Attention

链接: https://arxiv.org/abs/2405.03025
作者: Yu Gao, Jiancheng Huang, Xiaopeng Sun, Zequn Jie, Yujie Zhong, Lin Ma
备注:

点击查看摘要

Abstract:In this paper, we introduce Matten, a cutting-edge latent diffusion model with Mamba-Attention architecture for video generation. With minimal computational cost, Matten employs spatial-temporal attention for local video content modeling and bidirectional Mamba for global video content modeling. Our comprehensive experimental evaluation demonstrates that Matten has competitive performance with the current Transformer-based and GAN-based models in benchmark performance, achieving superior FVD scores and efficiency. Additionally, we observe a direct positive correlation between the complexity of our designed model and the improvement in video quality, indicating the excellent scalability of Matten.

CV-67-标题: AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation

链接: https://arxiv.org/abs/2405.03011
作者: Viet-Thanh Nguyen, Van-Truong Pham, Thi-Thao Tran
备注: 15 pages, 7 figures, 4 tables

点击查看摘要

Abstract:Skin lesion segmentation is a critical task in computer-aided diagnosis systems for dermatological diseases. Accurate segmentation of skin lesions from medical images is essential for early detection, diagnosis, and treatment planning. In this paper, we propose a new model for skin lesion segmentation namely AC-MambaSeg, an enhanced model that has the hybrid CNN-Mamba backbone, and integrates advanced components such as Convolutional Block Attention Module (CBAM), Attention Gate, and Selective Kernel Bottleneck. AC-MambaSeg leverages the Vision Mamba framework for efficient feature extraction, while CBAM and Selective Kernel Bottleneck enhance its ability to focus on informative regions and suppress background noise. We evaluate the performance of AC-MambaSeg on diverse datasets of skin lesion images including ISIC-2018 and PH2; then compare it against existing segmentation methods. Our model shows promising potential for improving computer-aided diagnosis systems and facilitating early detection and treatment of dermatological diseases. Our source code will be made available at: this https URL.

CV-68-标题: Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories

链接: https://arxiv.org/abs/2405.02982
作者: Xin Jin, Qianqian Qiao, Yi Lu, Shan Gao, Heng Huang, Guangdong Li
备注:

点击查看摘要

Abstract:Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relatively mature research environment. However, in contrast to the extensive research in photographic aesthetics, the field of aesthetic evaluation for paintings and Drawings has seen limited attention until the introduction of the BAID dataset in March 2023. This dataset solely comprises overall scores for high-quality artistic images. Our research marks the pioneering introduction of a multi-attribute, multi-category dataset specifically tailored to the field of painting: Aesthetics of Paintings and Drawings Dataset (APDD). The construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. This dataset encompasses 24 distinct artistic categories and 10 different aesthetic attributes. Each image in APDD has been evaluated by six professionally trained experts in the field of art, including assessments for both total aesthetic scores and aesthetic attribute scores. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries. Concurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. Through this research, our goal is to catalyze advancements in the field of aesthetic evaluation for paintings and drawings, while enriching the available resources and methodologies for its further development and application.

CV-69-标题: SkelCap: Automated Generation of Descriptive Text from Skeleton Keypoint Sequences

链接: https://arxiv.org/abs/2405.02977
作者: Ali Emre Keskin, Hacer Yalim Keles
备注: 8 pages, 5 figures, 7 tables, submitted to IEEE conference

点击查看摘要

Abstract:Numerous sign language datasets exist, yet they typically cover only a limited selection of the thousands of signs used globally. Moreover, creating diverse sign language datasets is an expensive and challenging task due to the costs associated with gathering a varied group of signers. Motivated by these challenges, we aimed to develop a solution that addresses these limitations. In this context, we focused on textually describing body movements from skeleton keypoint sequences, leading to the creation of a new dataset. We structured this dataset around AUTSL, a comprehensive isolated Turkish sign language dataset. We also developed a baseline model, SkelCap, which can generate textual descriptions of body movements. This model processes the skeleton keypoints data as a vector, applies a fully connected layer for embedding, and utilizes a transformer neural network for sequence-to-sequence modeling. We conducted extensive evaluations of our model, including signer-agnostic and sign-agnostic assessments. The model achieved promising results, with a ROUGE-L score of 0.98 and a BLEU-4 score of 0.94 in the signer-agnostic evaluation. The dataset we have prepared, namely the AUTSL-SkelCap, will be made publicly available soon.

CV-70-标题: VectorPainter: A Novel Approach to Stylized Vector Graphics Synthesis with Vectorized Strokes

链接: https://arxiv.org/abs/2405.02962
作者: Juncheng Hu, Ximing Xing, Zhengqi Zhang, Jing Zhang, Qian Yu
备注:

点击查看摘要

Abstract:We propose a novel method, VectorPainter, for the task of stylized vector graphics synthesis. Given a text prompt and a reference style image, VectorPainter generates a vector graphic that aligns in content with the text prompt and remains faithful in style to the reference image. We recognize that the key to this task lies in fully leveraging the intrinsic properties of vector graphics. Innovatively, we conceptualize the stylization process as the rearrangement of vectorized strokes extracted from the reference image. VectorPainter employs an optimization-based pipeline. It begins by extracting vectorized strokes from the reference image, which are then used to initialize the synthesis process. To ensure fidelity to the reference style, a novel style preservation loss is introduced. Extensive experiments have been conducted to demonstrate that our method is capable of aligning with the text description while remaining faithful to the reference image.

CV-71-标题: JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos

链接: https://arxiv.org/abs/2405.02961
作者: Pietro Nardelli, Danilo Comminiello
备注: Submitted to the International Journal of Computer Vision

点击查看摘要

Abstract:Due to the ever-increasing availability of video surveillance cameras and the growing need for crime prevention, the violence detection task is attracting greater attention from the research community. With respect to other action recognition tasks, violence detection in surveillance videos shows additional issues, such as the presence of a significant variety of real fight scenes. Unfortunately, available datasets seem to be very small compared with other action recognition datasets. Moreover, in surveillance applications, people in the scenes always differ for each video and the background of the footage differs for each camera. Also, violent actions in real-life surveillance videos must be detected quickly to prevent unwanted consequences, thus models would definitely benefit from a reduction in memory usage and computational costs. Such problems make classical action recognition methods difficult to be adopted. To tackle all these issues, we introduce JOSENet, a novel self-supervised framework that provides outstanding performance for violence detection in surveillance videos. The proposed model receives two spatiotemporal video streams, i.e., RGB frames and optical flows, and involves a new regularized self-supervised learning approach for videos. JOSENet provides improved performance compared to self-supervised state-of-the-art methods, while requiring one-fourth of the number of frames per video segment and a reduced frame rate. The source code and the instructions to reproduce our experiments are available at this https URL.

CV-72-标题: Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction

链接: https://arxiv.org/abs/2405.02958
作者: Xiaoyu Qiao, Weisheng Li, Yuping Huang, Lijian Yang
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

点击查看摘要

Abstract:Score matching with Langevin dynamics (SMLD) method has been successfully applied to accelerated MRI. However, the hyperparameters in the sampling process require subtle tuning, otherwise the results can be severely corrupted by hallucination artifacts, particularly with out-of-distribution test data. In this study, we propose a novel workflow in which SMLD results are regarded as additional priors to guide model-driven network training. First, we adopted a pretrained score network to obtain samples as preliminary guidance images (PGI) without the need for network retraining, parameter tuning and in-distribution test data. Although PGIs are corrupted by hallucination artifacts, we believe that they can provide extra information through effective denoising steps to facilitate reconstruction. Therefore, we designed a denoising module (DM) in the second step to improve the quality of PGIs. The features are extracted from the components of Langevin dynamics and the same score network with fine-tuning; hence, we can directly learn the artifact patterns. Third, we designed a model-driven network whose training is guided by denoised PGIs (DGIs). DGIs are densely connected with intermediate reconstructions in each cascade to enrich the features and are periodically updated to provide more accurate guidance. Our experiments on different sequences revealed that despite the low average quality of PGIs, the proposed workflow can effectively extract valuable information to guide the network training, even with severely reduced training data and sampling steps. Our method outperforms other cutting-edge techniques by effectively mitigating hallucination artifacts, yielding robust and high-quality reconstruction results.

CV-73-标题: Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training ICCV

链接: https://arxiv.org/abs/2405.02954
作者: Wenyu Zhang, Li Shen, Chuan-Sheng Foo
备注: Extension of ICCV paper arXiv:2212.07585, submitted to IJCV

点击查看摘要

Abstract:Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain. While the source model is a key avenue for acquiring target pseudolabels, the generated pseudolabels may exhibit source bias. In the conventional SFDA pipeline, a large data (e.g. ImageNet) pre-trained feature extractor is used to initialize the source model at the start of source training, and subsequently discarded. Despite having diverse features important for generalization, the pre-trained feature extractor can overfit to the source data distribution during source training and forget relevant target domain knowledge. Rather than discarding this valuable knowledge, we introduce an integrated framework to incorporate pre-trained networks into the target adaptation process. The proposed framework is flexible and allows us to plug modern pre-trained networks into the adaptation process to leverage their stronger representation learning capabilities. For adaptation, we propose the Co-learn algorithm to improve target pseudolabel quality collaboratively through the source model and a pre-trained feature extractor. Building on the recent success of the vision-language model CLIP in zero-shot image recognition, we present an extension Co-learn++ to further incorporate CLIP’s zero-shot classification decisions. We evaluate on 3 benchmark datasets and include more challenging scenarios such as open-set, partial-set and open-partial SFDA. Experimental results demonstrate that our proposed strategy improves adaptation performance and can be successfully integrated with existing SFDA methods.

CV-74-标题: iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval ICCV2023

链接: https://arxiv.org/abs/2405.02951
作者: Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, Alberto Del Bimbo
备注: Extended version of the ICCV2023 paper arXiv:2303.15247

点击查看摘要

Abstract:Given a query consisting of a reference image and a relative caption, Composed Image Retrieval (CIR) aims to retrieve target images visually similar to the reference one while incorporating the changes specified in the relative caption. The reliance of supervised methods on labor-intensive manually labeled datasets hinders their broad applicability. In this work, we introduce a new task, Zero-Shot CIR (ZS-CIR), that addresses CIR without the need for a labeled training dataset. We propose an approach named iSEARLE (improved zero-Shot composEd imAge Retrieval with textuaL invErsion) that involves mapping the visual information of the reference image into a pseudo-word token in CLIP token embedding space and combining it with the relative caption. To foster research on ZS-CIR, we present an open-domain benchmarking dataset named CIRCO (Composed Image Retrieval on Common Objects in context), the first CIR dataset where each query is labeled with multiple ground truths and a semantic categorization. The experimental results illustrate that iSEARLE obtains state-of-the-art performance on three different CIR datasets – FashionIQ, CIRR, and the proposed CIRCO – and two additional evaluation settings, namely domain conversion and object composition. The dataset, the code, and the model are publicly available at this https URL.

CV-75-标题: Invertible Residual Rescaling Models

链接: https://arxiv.org/abs/2405.02945
作者: Jinmin Li, Tao Dai, Yaohua Zha, Yilu Luo, Longfei Lu, Bin Chen, Zhi Wang, Shu-Tao Xia, Jingyun Zhang
备注:

点击查看摘要

Abstract:Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs with deeper networks are difficult to train, thus hindering the representational ability of IRNs. To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution. Specifically, we propose IRRM to build a deep network, which contains several Residual Downscaling Modules (RDMs) with long skip connections. Each RDM consists of several Invertible Residual Blocks (IRBs) with short connections. In this way, RDM allows rich low-frequency information to be bypassed by skip connections and forces models to focus on extracting high-frequency information from the image. Extensive experiments show that our IRRM performs significantly better than other state-of-the-art methods with much fewer parameters and complexity. Particularly, our IRRM has respectively PSNR gains of at least 0.3 dB over HCFlow and IRN in the \times 4 rescaling while only using 60% parameters and 50% FLOPs. The code will be available at this https URL.

CV-76-标题: Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters CVPR2024

链接: https://arxiv.org/abs/2405.02944
作者: Xiwen Chen, Wenhui Zhu, Peijie Qiu, Abolfazl Razi
备注: Accepted by PBDL-CVPR 2024

点击查看摘要

Abstract:Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent solutions. The best solution inherently depends on prior knowledge or assumptions, such as the sparsity of the image. Furthermore, the reconstruction process for most IIPs relies significantly on the imaging (i.e. forward model) parameters, which might not be fully known, or the measurement device may undergo calibration drifts. These uncertainties in the forward model create substantial challenges, where inaccurate reconstructions usually happen when the postulated parameters of the forward model do not fully match the actual ones. In this work, we devoted to tackling accurate reconstruction under the context of a set of possible forward model parameters that exist. Here, we propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. Specifically, our method can reconstruct the signal by considering all candidate parameters of the forward model simultaneously during the update of the neural network. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters. Proof-of-concept experiments demonstrate that the proposed MA achieves performance comparable to the forward model with the known precise parameter in reconstruction across both compressive sensing and phase retrieval applications, with a PSNR gap of 0.17 to 1.94 over various datasets, including MNIST, X-ray, Glas, and MoNuseg. This highlights our method’s significant potential in reconstruction under an uncertain forward model.

CV-77-标题: Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling

链接: https://arxiv.org/abs/2405.02941
作者: Jinmin Li, Tao Dai, Jingyun Zhang, Kang Liu, Jun Wang, Shaoming Wang, Shu-Tao Xia, rizen guo
备注:

点击查看摘要

Abstract:Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling. However, IRN-based methods tend to produce over-smoothed results, while GAN-based methods easily generate fake details, which thus hinders their real applications. To address this issue, we propose Boundary-aware Decoupled Flow Networks (BDFlow) to generate realistic and visually pleasing results. Unlike previous methods that model high-frequency information as standard Gaussian distribution directly, our BDFlow first decouples the high-frequency information into \textitsemantic high-frequency that adheres to a Boundary distribution and \textitnon-semantic high-frequency counterpart that adheres to a Gaussian distribution. Specifically, to capture semantic high-frequency parts accurately, we use Boundary-aware Mask (BAM) to constrain the model to produce rich textures, while non-semantic high-frequency part is randomly sampled from a Gaussian distribution.Comprehensive experiments demonstrate that our BDFlow significantly outperforms other state-of-the-art methods while maintaining lower complexity. Notably, our BDFlow improves the PSNR by 4.4 dB and the SSIM by 0.1 on average over GRAIN, utilizing only 74% of the parameters and 20% of the computation. The code will be available at this https URL.

CV-78-标题: Unified Dynamic Scanpath Predictors Outperform Individually Trained Models

链接: https://arxiv.org/abs/2405.02929
作者: Fares Abawi, Di Fu, Stefan Wermter
备注:

点击查看摘要

Abstract:Previous research on scanpath prediction has mainly focused on group models, disregarding the fact that the scanpaths and attentional behaviors of individuals are diverse. The disregard of these differences is especially detrimental to social human-robot interaction, whereby robots commonly emulate human gaze based on heuristics or predefined patterns. However, human gaze patterns are heterogeneous and varying behaviors can significantly affect the outcomes of such human-robot interactions. To fill this gap, we developed a deep learning-based social cue integration model for saliency prediction to instead predict scanpaths in videos. Our model learned scanpaths by recursively integrating fixation history and social cues through a gating mechanism and sequential attention. We evaluated our approach on gaze datasets of dynamic social scenes, observed under the free-viewing condition. The introduction of fixation history into our models makes it possible to train a single unified model rather than the resource-intensive approach of training individual models for each set of scanpaths. We observed that the late neural integration approach surpasses early fusion when training models on a large dataset, in comparison to a smaller dataset with a similar distribution. Results also indicate that a single unified model, trained on all the observers’ scanpaths, performs on par or better than individually trained models. We hypothesize that this outcome is a result of the group saliency representations instilling universal attention in the model, while the supervisory signal guides it to learn personalized attentional behaviors, providing the unified model a benefit over individual models due to its implicit representation of universal attention.

CV-79-标题: MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging

链接: https://arxiv.org/abs/2405.02918
作者: Yuanye Liu, Zheyao Gao, Nannan Shi, Fuping Wu, Yuxin Shi, Qingchao Chen, Xiahai Zhuang
备注: Submitted to Medical Image Analysis

点击查看摘要

Abstract:Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches simultaneously. However, previous multi-view approaches could not typically calculate uncertainty by nature, and they generally integrate features from different views in a black-box fashion, hence compromising reliability as well as interpretability of the resulting models. In this work, we propose a new multi-view method based on evidential learning, referred to as MERIT, which tackles the two challenges in a unified framework. MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability. Specifically, MERIT models the prediction from each sub-view as an opinion with quantified uncertainty under the guidance of the subjective logic theory. Furthermore, a distribution-aware base rate is introduced to enhance performance, particularly in scenarios involving class distribution shifts. Finally, MERIT adopts a feature-specific combination rule to explicitly fuse multi-view predictions, thereby enhancing interpretability. Results have showcased the effectiveness of the proposed MERIT, highlighting the reliability and offering both ad-hoc and post-hoc interpretability. They also illustrate that MERIT can elucidate the significance of each view in the decision-making process for liver fibrosis staging.

CV-80-标题: Fast TILs estimation in lung cancer WSIs based on semi-stochastic patch sampling

链接: https://arxiv.org/abs/2405.02913
作者: Nikita Shvetsov, Anders Sildnes, Lill-Tove Rasmussen Busund, Stig Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas K. Kilvaer
备注: 18 pages, 7 figures, 6 appendix pages

点击查看摘要

Abstract:Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification in whole slide images (WSIs) is laborious and subject to variability, potentially undermining patient outcomes. Our study introduces an automated pipeline that utilizes semi-stochastic patch sampling, patch classification to retain prognostically relevant patches, and cell quantification using the HoVer-Net model to streamline the TIL evaluation process. This pipeline efficiently excludes approximately 70% of areas not relevant for prognosis and requires only 5% of the remaining patches to maintain prognostic accuracy (c-index 0.65 ± 0.01). The computational efficiency achieved does not sacrifice prognostic accuracy, as demonstrated by the TILs score’s strong correlation with patient survival, which surpasses traditional CD8 IHC scoring methods. While the pipeline demonstrates potential for enhancing NSCLC prognostication and personalization of treatment, comprehensive clinical validation is still required. Future research should focus on verifying its broader clinical utility and investigating additional biomarkers to improve NSCLC prognosis.

CV-81-标题: Multimodal Sense-Informed Prediction of 3D Human Motions

链接: https://arxiv.org/abs/2405.02911
作者: Zhenyu Lou, Qiongjie Cui, Haofan Wang, Xu Tang, Hong Zhou
备注:

点击查看摘要

Abstract:Predicting future human pose is a fundamental application for machine intelligence, which drives robots to plan their behavior and paths ahead of time to seamlessly accomplish human-robot collaboration in real-world 3D scenarios. Despite encouraging results, existing approaches rarely consider the effects of the external scene on the motion sequence, leading to pronounced artifacts and physical implausibilities in the predictions. To address this limitation, this work introduces a novel multi-modal sense-informed motion prediction approach, which conditions high-fidelity generation on two modal information: external 3D scene, and internal human gaze, and is able to recognize their salience for future human activity. Furthermore, the gaze information is regarded as the human intention, and combined with both motion and scene features, we construct a ternary intention-aware attention to supervise the generation to match where the human wants to reach. Meanwhile, we introduce semantic coherence-aware attention to explicitly distinguish the salient point clouds and the underlying ones, to ensure a reasonable interaction of the generated sequence with the 3D scene. On two real-world benchmarks, the proposed method achieves state-of-the-art performance both in 3D human pose and trajectory prediction.

CV-82-标题: SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection

链接: https://arxiv.org/abs/2405.02906
作者: Kassaw Abraham Mulat, Zhengyong Feng, Tegegne Solomon Eshetie, Ahmed Endris Hasen
备注: 9 pages, 5 figures

点击查看摘要

Abstract:Salient object detection (SOD) remains an important task in computer vision, with applications ranging from image segmentation to autonomous driving. Fully convolutional network (FCN)-based methods have made remarkable progress in visual saliency detection over the last few decades. However, these methods have limitations in accurately detecting salient objects, particularly in challenging scenes with multiple objects, small objects, or objects with low resolutions. To address this issue, we proposed a Saliency Fusion Attention U-Net (SalFAU-Net) model, which incorporates a saliency fusion module into each decoder block of the attention U-net model to generate saliency probability maps from each decoder block. SalFAU-Net employs an attention mechanism to selectively focus on the most informative regions of an image and suppress non-salient regions. We train SalFAU-Net on the DUTS dataset using a binary cross-entropy loss function. We conducted experiments on six popular SOD evaluation datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method, SalFAU-Net, achieves competitive performance compared to other methods in terms of mean absolute error (MAE), F-measure, s-measure, and e-measure.

CV-83-标题: A drone detector with modified backbone and multiple pyramid featuremaps enhancement structure (MDDPE)

链接: https://arxiv.org/abs/2405.02882
作者: Chenhao Wu
备注: 20 pages, 10 figures

点击查看摘要

Abstract:This work presents a drone detector with modified backbone and multiple pyramid feature maps enhancement structure (MDDPE). Novel feature maps improve modules that uses different levels of information to produce more robust and discriminatory features is proposed. These module includes the feature maps supplement function and the feature maps recombination enhancement this http URL effectively handle the drone characteristics, auxiliary supervisions that are implemented in the early stages by employing tailored anchors designed are utilized. To further improve the modeling of real drone detection scenarios and initialization of the regressor, an updated anchor matching technique is introduced to match anchors and ground truth drone as closely as feasible. To show the proposed MDDPE’s superiority over the most advanced detectors, extensive experiments are carried out using well-known drone detection benchmarks.

CV-84-标题: Blending Distributed NeRFs with Tri-stage Robust Pose Optimization

链接: https://arxiv.org/abs/2405.02880
作者: Baijun Ye, Caiyun Liu, Xiaoyu Ye, Yuantao Chen, Yuhai Wang, Zike Yan, Yongliang Shi, Hao Zhao, Guyue Zhou
备注:

点击查看摘要

Abstract:Due to the limited model capacity, leveraging distributed Neural Radiance Fields (NeRFs) for modeling extensive urban environments has become a necessity. However, current distributed NeRF registration approaches encounter aliasing artifacts, arising from discrepancies in rendering resolutions and suboptimal pose precision. These factors collectively deteriorate the fidelity of pose estimation within NeRF frameworks, resulting in occlusion artifacts during the NeRF blending stage. In this paper, we present a distributed NeRF system with tri-stage pose optimization. In the first stage, precise poses of images are achieved by bundle adjusting Mip-NeRF 360 with a coarse-to-fine strategy. In the second stage, we incorporate the inverting Mip-NeRF 360, coupled with the truncated dynamic low-pass filter, to enable the achievement of robust and precise poses, termed Frame2Model optimization. On top of this, we obtain a coarse transformation between NeRFs in different coordinate systems. In the third stage, we fine-tune the transformation between NeRFs by Model2Model pose optimization. After obtaining precise transformation parameters, we proceed to implement NeRF blending, showcasing superior performance metrics in both real-world and simulation scenarios. Codes and data will be publicly available at this https URL.

CV-85-标题: MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior

链接: https://arxiv.org/abs/2405.02859
作者: Honghua Chen, Chen Change Loy, Xingang Pan
备注: 14 pages, 10 figures, conference

点击查看摘要

Abstract:Despite the emergence of successful NeRF inpainting methods built upon explicit RGB and depth 2D inpainting supervisions, these methods are inherently constrained by the capabilities of their underlying 2D inpainters. This is due to two key reasons: (i) independently inpainting constituent images results in view-inconsistent imagery, and (ii) 2D inpainters struggle to ensure high-quality geometry completion and alignment with inpainted RGB images. To overcome these limitations, we propose a novel approach called MVIP-NeRF that harnesses the potential of diffusion priors for NeRF inpainting, addressing both appearance and geometry aspects. MVIP-NeRF performs joint inpainting across multiple views to reach a consistent solution, which is achieved via an iterative optimization process based on Score Distillation Sampling (SDS). Apart from recovering the rendered RGB images, we also extract normal maps as a geometric representation and define a normal SDS loss that motivates accurate geometry inpainting and alignment with the appearance. Additionally, we formulate a multi-view SDS score function to distill generative priors simultaneously from different view images, ensuring consistent visual completion when dealing with large view variations. Our experimental results show better appearance and geometry recovery than previous NeRF inpainting methods.

CV-86-标题: SMCD: High Realism Motion Style Transfer via Mamba-based Diffusion

链接: https://arxiv.org/abs/2405.02844
作者: Ziyun Qian, Zeyu Xiao, Zhenyi Wu, Dingkang Yang, Mingcheng Li, Shunli Wang, Shuaibing Wang, Dongliang Kou, Lihua Zhang
备注:

点击查看摘要

Abstract:Motion style transfer is a significant research direction in multimedia applications. It enables the rapid switching of different styles of the same motion for virtual digital humans, thus vastly increasing the diversity and realism of movements. It is widely applied in multimedia scenarios such as movies, games, and the Metaverse. However, most of the current work in this field adopts the GAN, which may lead to instability and convergence issues, making the final generated motion sequence somewhat chaotic and unable to reflect a highly realistic and natural style. To address these problems, we consider style motion as a condition and propose the Style Motion Conditioned Diffusion (SMCD) framework for the first time, which can more comprehensively learn the style features of motion. Moreover, we apply Mamba model for the first time in the motion style transfer field, introducing the Motion Style Mamba (MSM) module to handle longer motion sequences. Thirdly, aiming at the SMCD framework, we propose Diffusion-based Content Consistency Loss and Content Consistency Loss to assist the overall framework’s training. Finally, we conduct extensive experiments. The results reveal that our method surpasses state-of-the-art methods in both qualitative and quantitative comparisons, capable of generating more realistic motion sequences.

CV-87-标题: Residual-Conditioned Optimal Transport: Towards Structure-preserving Unpaired and Paired Image Restoration ICML2024

链接: https://arxiv.org/abs/2405.02843
作者: Xiaole Tang, Xin Hu, Xiang Gu, Jian Sun
备注: ICML 2024

点击查看摘要

Abstract:Deep learning-based image restoration methods have achieved promising performance. However, how to faithfully preserve the structure of the original image remains challenging. To address this challenge, we propose a novel Residual-Conditioned Optimal Transport (RCOT) approach, which models the image restoration as an optimal transport (OT) problem for both unpaired and paired settings, integrating the transport residual as a unique degradation-specific cue for both the transport cost and the transport map. Specifically, we first formalize a Fourier residual-guided OT objective by incorporating the degradation-specific information of the residual into the transport cost. Based on the dual form of the OT formulation, we design the transport map as a two-pass RCOT map that comprises a base model and a refinement process, in which the transport residual is computed by the base model in the first pass and then encoded as a degradation-specific embedding to condition the second-pass restoration. By duality, the RCOT problem is transformed into a minimax optimization problem, which can be solved by adversarially training neural networks. Extensive experiments on multiple restoration tasks show the effectiveness of our approach in terms of both distortion measures and perceptual quality. Particularly, RCOT restores images with more faithful structural details compared to state-of-the-art methods.

CV-88-标题: Scene-Adaptive Person Search via Bilateral Modulations

链接: https://arxiv.org/abs/2405.02834
作者: Yimin Jiang, Huibing Wang, Jinjia Peng, Xianping Fu, Yang Wang
备注:

点击查看摘要

Abstract:Person search aims to localize specific a target person from a gallery set of images with various scenes. As the scene of moving pedestrian changes, the captured person image inevitably bring in lots of background noise and foreground noise on the person feature, which are completely unrelated to the person identity, leading to severe performance degeneration. To address this issue, we present a Scene-Adaptive Person Search (SEAS) model by introducing bilateral modulations to simultaneously eliminate scene noise and maintain a consistent person representation to adapt to various scenes. In SEAS, a Background Modulation Network (BMN) is designed to encode the feature extracted from the detected bounding box into a multi-granularity embedding, which reduces the input of background noise from multiple levels with norm-aware. Additionally, to mitigate the effect of foreground noise on the person feature, SEAS introduces a Foreground Modulation Network (FMN) to compute the clutter reduction offset for the person embedding based on the feature map of the scene image. By bilateral modulations on both background and foreground within an end-to-end manner, SEAS obtains consistent feature representations without scene noise. SEAS can achieve state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU with 97.1% mAP and PRW with 60.5% mAP. The code is available at this https URL.

CV-89-标题: Fast One-Stage Unsupervised Domain Adaptive Person Search

链接: https://arxiv.org/abs/2405.02832
作者: Tianxiang Cui, Huibing Wang, Jinjia Peng, Ruoxi Deng, Xianping Fu, Yang Wang
备注:

点击查看摘要

Abstract:Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increases model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementary integrates domain adaptaion with label adaptaion within an end-to-end manner without iterative clustering. To minimize the domain discrepancy, FOUS introduced an Attention-based Domain Alignment Module (ADAM) which can not only align various domains for both detection and ReID tasks but also construct an attention mechanism to reduce the adverse impacts of low-quality candidates resulting from unsupervised detection. Moreover, to avoid the redundant iterative clustering mode, FOUS adopts a prototype-guided labeling method which minimizes redundant correlation computations for partial samples and assigns noisy coarse label groups efficiently. The coarse label groups will be continuously refined via label-flexible training network with an adaptive selection strategy. With the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available at this https URL.

CV-90-标题: You Only Need Half: Boosting Data Augmentation by Using Partial Content

链接: https://arxiv.org/abs/2405.02830
作者: Juntao Hu, Yuan Wu
备注: Technical report,16 pages

点击查看摘要

Abstract:We propose a novel data augmentation method termed You Only Need hAlf (YONA), which simplifies the augmentation process. YONA bisects an image, substitutes one half with noise, and applies data augmentation techniques to the remaining half. This method reduces the redundant information in the original image, encourages neural networks to recognize objects from incomplete views, and significantly enhances neural networks’ robustness. YONA is distinguished by its properties of parameter-free, straightforward application, enhancing various existing data augmentation strategies, and thereby bolstering neural networks’ robustness without additional computational cost. To demonstrate YONA’s efficacy, extensive experiments were carried out. These experiments confirm YONA’s compatibility with diverse data augmentation methods and neural network architectures, yielding substantial improvements in CIFAR classification tasks, sometimes outperforming conventional image-level data augmentation methods. Furthermore, YONA markedly increases the resilience of neural networks to adversarial attacks. Additional experiments exploring YONA’s variants conclusively show that masking half of an image optimizes performance. The code is available at this https URL.

CV-91-标题: Adaptive Guidance Learning for Camouflaged Object Detection

链接: https://arxiv.org/abs/2405.02824
作者: Zhennan Chen, Xuying Zhang, Tian-Zhu Xiang, Ying Tai
备注:

点击查看摘要

Abstract:Camouflaged object detection (COD) aims to segment objects visually embedded in their surroundings, which is a very challenging task due to the high similarity between the objects and the background. To address it, most methods often incorporate additional information (e.g., boundary, texture, and frequency clues) to guide feature learning for better detecting camouflaged objects from the background. Although progress has been made, these methods are basically individually tailored to specific auxiliary cues, thus lacking adaptability and not consistently achieving high segmentation performance. To this end, this paper proposes an adaptive guidance learning network, dubbed \textitAGLNet, which is a unified end-to-end learnable model for exploring and adapting different additional cues in CNN models to guide accurate camouflaged feature learning. Specifically, we first design a straightforward additional information generation (AIG) module to learn additional camouflaged object cues, which can be adapted for the exploration of effective camouflaged features. Then we present a hierarchical feature combination (HFC) module to deeply integrate additional cues and image features to guide camouflaged feature learning in a multi-level fusion manner.Followed by a recalibration decoder (RD), different features are further aggregated and refined for accurate object prediction. Extensive experiments on three widely used COD benchmark datasets demonstrate that the proposed method achieves significant performance improvements under different additional cues, and outperforms the recent 20 state-of-the-art methods by a large margin. Our code will be made publicly available at: \textcolorbluethis https URL.

CV-92-标题: Region-specific Risk Quantification for Interpretable Prognosis of COVID-19

链接: https://arxiv.org/abs/2405.02815
作者: Zhusi Zhong, Jie Li, Zhuoqi Ma, Scott Collins, Harrison Bai, Paul Zhang, Terrance Healey, Xinbo Gao, Michael K. Atalay, Zhicheng Jiao
备注:

点击查看摘要

Abstract:The COVID-19 pandemic has strained global public health, necessitating accurate diagnosis and intervention to control disease spread and reduce mortality rates. This paper introduces an interpretable deep survival prediction model designed specifically for improved understanding and trust in COVID-19 prognosis using chest X-ray (CXR) images. By integrating a large-scale pretrained image encoder, Risk-specific Grad-CAM, and anatomical region detection techniques, our approach produces regional interpretable outcomes that effectively capture essential disease features while focusing on rare but critical abnormal regions. Our model’s predictive results provide enhanced clarity and transparency through risk area localization, enabling clinicians to make informed decisions regarding COVID-19 diagnosis with better understanding of prognostic insights. We evaluate the proposed method on a multi-center survival dataset and demonstrate its effectiveness via quantitative and qualitative assessments, achieving superior C-indexes (0.764 and 0.727) and time-dependent AUCs (0.799 and 0.691). These results suggest that our explainable deep survival prediction model surpasses traditional survival analysis methods in risk prediction, improving interpretability for clinical decision making and enhancing AI system trustworthiness.

CV-93-标题: PV Transformer : Point-to-Voxel Transformer for Scalable 3D Object Detection

链接: https://arxiv.org/abs/2405.02811
作者: Zhaoqi Leng, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan
备注:

点击查看摘要

Abstract:3D object detectors for point clouds often rely on a pooling-based PointNet to encode sparse points into grid-like voxels or pillars. In this paper, we identify that the common PointNet design introduces an information bottleneck that limits 3D object detection accuracy and scalability. To address this limitation, we propose PVTransformer: a transformer-based point-to-voxel architecture for 3D detection. Our key idea is to replace the PointNet pooling operation with an attention module, leading to a better point-to-voxel aggregation function. Our design respects the permutation invariance of sparse 3D points while being more expressive than the pooling-based PointNet. Experimental results show our PVTransformer achieves much better performance compared to the latest 3D object detectors. On the widely used Waymo Open Dataset, our PVTransformer achieves state-of-the-art 76.5 mAPH L2, outperforming the prior art of SWFormer by +1.7 mAPH L2.

CV-94-标题: Kinematic analysis of structural mechanics based on convolutional neural network

链接: https://arxiv.org/abs/2405.02807
作者: Leye Zhang, Xiangxiang Tian, Hongjun Zhang
备注: 9 pages, 13 figures

点击查看摘要

Abstract:Attempt to use convolutional neural network to achieve kinematic analysis of plane bar structure. Through 3dsMax animation software and OpenCV module, self-build image dataset of geometrically stable system and geometrically unstable system. we construct and train convolutional neural network model based on the TensorFlow and Keras deep learning platform framework. The model achieves 100% accuracy on the training set, validation set, and test set. The accuracy on the additional test set is 93.7%, indicating that convolutional neural network can learn and master the relevant knowledge of kinematic analysis of structural mechanics. In the future, the generalization ability of the model can be improved through the diversity of dataset, which has the potential to surpass human experts for complex structures. Convolutional neural network has certain practical value in the field of kinematic analysis of structural mechanics. Using visualization technology, we reveal how convolutional neural network learns and recognizes structural features. Using pre-trained VGG16 model for feature extraction and fine-tuning, we found that the generalization ability is inferior to the self-built model.

CV-95-标题: Adapting to Distribution Shift by Visual Domain Prompt Generation ICLR2024

链接: https://arxiv.org/abs/2405.02797
作者: Zhixiang Chi, Li Gu, Tao Zhong, Huan Liu, Yuanhao Yu, Konstantinos N Plataniotis, Yang Wang
备注: ICLR2024, code: this https URL

点击查看摘要

Abstract:In this paper, we aim to adapt a model at test-time using a few unlabeled data to address distribution shifts. To tackle the challenges of extracting domain knowledge from a limited amount of data, it is crucial to utilize correlated information from pre-trained backbones and source domains. Previous studies fail to utilize recent foundation models with strong out-of-distribution generalization. Additionally, domain-centric designs are not flavored in their works. Furthermore, they employ the process of modelling source domains and the process of learning to adapt independently into disjoint training stages. In this work, we propose an approach on top of the pre-computed features of the foundation model. Specifically, we build a knowledge bank to learn the transferable knowledge from source domains. Conditioned on few-shot target data, we introduce a domain prompt generator to condense the knowledge bank into a domain-specific prompt. The domain prompt then directs the visual features towards a particular domain via a guidance module. Moreover, we propose a domain-aware contrastive loss and employ meta-learning to facilitate domain knowledge extraction. Extensive experiments are conducted to validate the domain knowledge extraction. The proposed method outperforms previous work on 5 large-scale benchmarks including WILDS and DomainNet.

CV-96-标题: Jointly Learning Spatial Angular and Temporal Information for Enhanced Lane Detection

链接: https://arxiv.org/abs/2405.02792
作者: Muhammad Zeshan Alam
备注: 5 pages, 3 Figures , Accepted IEEE Conference on Signal Processing and Communications Applications

点击查看摘要

Abstract:This paper introduces a novel approach for enhanced lane detection by integrating spatial, angular, and temporal information through light field imaging and novel deep learning models. Utilizing lenslet-inspired 2D light field representations and LSTM networks, our method significantly improves lane detection in challenging conditions. We demonstrate the efficacy of this approach with modified CNN architectures, showing superior per- formance over traditional methods. Our findings suggest this integrated data approach could advance lane detection technologies and inspire new models that leverage these multidimensional insights for autonomous vehicle percep- tion.

CV-97-标题: Efficient Text-driven Motion Generation via Latent Consistency Training

链接: https://arxiv.org/abs/2405.02791
作者: Mengxian Hu, Minghao Zhu, Xun Zhou, Qingqing Yan, Shu Li, Chengju Liu, Qijun Chen
备注:

点击查看摘要

Abstract:Motion diffusion models have recently proven successful for text-driven human motion generation. Despite their excellent generation performance, they are challenging to infer in real time due to the multi-step sampling mechanism that involves tens or hundreds of repeat function evaluation iterations. To this end, we investigate a motion latent consistency Training (MLCT) for motion generation to alleviate the computation and time consumption during iteration inference. It applies diffusion pipelines to low-dimensional motion latent spaces to mitigate the computational burden of each function evaluation. Explaining the diffusion process with probabilistic flow ordinary differential equation (PF-ODE) theory, the MLCT allows extremely few steps infer between the prior distribution to the motion latent representation distribution via maintaining consistency of the outputs over the trajectory of PF-ODE. Especially, we introduce a quantization constraint to optimize motion latent representations that are bounded, regular, and well-reconstructed compared to traditional variational constraints. Furthermore, we propose a conditional PF-ODE trajectory simulation method, which improves the conditional generation performance with minimal additional training costs. Extensive experiments on two human motion generation benchmarks show that the proposed model achieves state-of-the-art performance with less than 10% time cost.

CV-98-标题: Light Field Spatial Resolution Enhancement Framework

链接: https://arxiv.org/abs/2405.02787
作者: Javeria Shabbir, Muhammad Zeshan.Alam, M.Umair Mukati
备注: 5 pages, 6 figures, accepted in IEEE Conference on Signal Processing and Communications Applications

点击查看摘要

Abstract:Light field (LF) imaging captures both angular and spatial light distributions, enabling advanced photographic techniques. However, micro-lens array (MLA)- based cameras face a spatial-angular resolution tradeoff due to a single shared sensor. We propose a novel light field framework for resolution enhancement, employing a modular approach. The first module generates a high-resolution, all-in-focus image. The second module, a texture transformer network, enhances the resolution of each light field perspective independently using the output of the first module as a reference image. The final module leverages light field regularity to jointly improve resolution across all LF image perspectives. Our approach demonstrates superior performance to existing methods in both qualitative and quantitative evaluations.

CV-99-标题: Fused attention mechanism-based ore sorting network

链接: https://arxiv.org/abs/2405.02785
作者: Junjiang Zhen, Bojun Xie
备注:

点击查看摘要

Abstract:Deep learning has had a significant impact on the identification and classification of mineral resources, especially playing a key role in efficiently and accurately identifying different minerals, which is important for improving the efficiency and accuracy of mining. However, traditional ore sorting meth- ods often suffer from inefficiency and lack of accuracy, especially in complex mineral environments. To address these challenges, this study proposes a method called OreYOLO, which incorporates an attentional mechanism and a multi-scale feature fusion strategy, based on ore data from gold and sul- fide ores. By introducing the progressive feature pyramid structure into YOLOv5 and embedding the attention mechanism in the feature extraction module, the detection performance and accuracy of the model are greatly improved. In order to adapt to the diverse ore sorting scenarios and the deployment requirements of edge devices, the network structure is designed to be lightweight, which achieves a low number of parameters (3.458M) and computational complexity (6.3GFLOPs) while maintaining high accuracy (99.3% and 99.2%, respectively). In the experimental part, a target detection dataset containing 6000 images of gold and sulfuric iron ore is constructed for gold and sulfuric iron ore classification training, and several sets of comparison experiments are set up, including the YOLO series, EfficientDet, Faster-RCNN, and CenterNet, etc., and the experiments prove that OreYOLO outperforms the commonly used high-performance object detection of these architectures

CV-100-标题: A self-supervised text-vision framework for automated brain abnormality detection

链接: https://arxiv.org/abs/2405.02782
作者: David A. Wood, Emily Guilhem, Sina Kafiabadi, Ayisha Al Busaidi, Kishan Dissanayake, Ahmed Hammam, Nina Mansoor, Matthew Townend, Siddharth Agarwal, Yiran Wei, Asif Mazumder, Gareth J. Barker, Peter Sasieni, Sebastien Ourselin, James H. Cole, Thomas C. Booth
备注: Under Review

点击查看摘要

Abstract:Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports. Our training approach consisted of two-steps. First, a dedicated neuroradiological language model - NeuroBERT - was trained to generate fixed-dimensional vector representations of neuroradiology reports (N = 50,523) via domain-specific self-supervised learning tasks. Next, convolutional neural networks (one per MRI sequence) learnt to map individual brain scans to their corresponding text vector representations by optimising a mean square error loss. Once trained, our text-vision framework can be used to detect abnormalities in unreported brain MRI examinations by scoring scans against suitable query sentences (e.g., ‘there is an acute stroke’, ‘there is hydrocephalus’ etc.), enabling a range of classification-based applications including automated triage. Potentially, our framework could also serve as a clinical decision support tool, not only by suggesting findings to radiologists and detecting errors in provisional reports, but also by retrieving and displaying examples of pathologies from historical examinations that could be relevant to the current case based on textual descriptors.

CV-101-标题: Instantaneous Perception of Moving Objects in 3D CVPR2024

链接: https://arxiv.org/abs/2405.02781
作者: Di Liu, Bingbing Zhuang, Dimitris N. Metaxas, Manmohan Chandraker
备注: CVPR 2024

点击查看摘要

Abstract:The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions, we contend that the instantaneous detection and quantification of subtle motions is equally important as they indicate the nuances in driving behavior that may be safety critical, such as behaviors near a stop sign of parking positions. We delve into this under-explored task, examining its unique challenges and developing our solution, accompanied by a carefully designed benchmark. Specifically, due to the lack of correspondences between consecutive frames of sparse Lidar point clouds, static objects might appear to be moving - the so-called swimming effect. This intertwines with the true object motion, thereby posing ambiguity in accurate estimation, especially for subtle motions. To address this, we propose to leverage local occupancy completion of object point clouds to densify the shape cue, and mitigate the impact of swimming artifacts. The occupancy completion is learned in an end-to-end fashion together with the detection of moving objects and the estimation of their motion, instantaneously as soon as objects start to move. Extensive experiments demonstrate superior performance compared to standard 3D motion estimation approaches, particularly highlighting our method’s specialized treatment of subtle motions.

CV-102-标题: MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning

链接: https://arxiv.org/abs/2405.02771
作者: Vishal Nedungadi, Ankit Kariryaa, Stefan Oehmcke, Serge Belongie, Christian Igel, Nico Lang
备注: Data and code is available on the project page: this https URL

点击查看摘要

Abstract:The volume of unlabelled Earth observation (EO) data is huge, but many important applications lack labelled training data. However, EO data offers the unique opportunity to pair data from different modalities and sensors automatically based on geographic location and time, at virtually no human labor cost. We seize this opportunity to create a diverse multi-modal pretraining dataset at global scale. Using this new corpus of 1.2 million locations, we propose a Multi-Pretext Masked Autoencoder (MP-MAE) approach to learn general-purpose representations for optical satellite images. Our approach builds on the ConvNeXt V2 architecture, a fully convolutional masked autoencoder (MAE). Drawing upon a suite of multi-modal pretext tasks, we demonstrate that our MP-MAE approach outperforms both MAEs pretrained on ImageNet and MAEs pretrained on domain-specific satellite images. This is shown on several downstream tasks including image classification and semantic segmentation. We find that multi-modal pretraining notably improves the linear probing performance, e.g. 4pp on BigEarthNet and 16pp on So2Sat, compared to pretraining on optical satellite images only. We show that this also leads to better label and parameter efficiency which are crucial aspects in global scale applications.

CV-103-标题: Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning

链接: https://arxiv.org/abs/2405.02766
作者: Fahad Sarfraz, Bahram Zonooz, Elahe Arani
备注: Accepted at 3rd Conference on Lifelong Learning Agents (CoLLAs), 2024

点击查看摘要

Abstract:While humans excel at continual learning (CL), deep neural networks (DNNs) exhibit catastrophic forgetting. A salient feature of the brain that allows effective CL is that it utilizes multiple modalities for learning and inference, which is underexplored in DNNs. Therefore, we study the role and interactions of multiple modalities in mitigating forgetting and introduce a benchmark for multimodal continual learning. Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations. This makes the model less vulnerable to modality-specific regularities and considerably mitigates forgetting. Furthermore, we observe that individual modalities exhibit varying degrees of robustness to distribution shift. Finally, we propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality. Our method sets a strong baseline that enables both single- and multimodal inference. Our study provides a promising case for further exploring the role of multiple modalities in enabling CL and provides a standard benchmark for future research.

CV-104-标题: TK-Planes: Tiered K-Planes with High Dimensional Feature Vectors for Dynamic UAV-based Scenes IROS2024

链接: https://arxiv.org/abs/2405.02762
作者: Christopher Maxey, Jaehoon Choi, Yonghan Lee, Hyungtae Lee, Dinesh Manocha, Heesung Kwon
备注: 8 pages, submitted to IROS2024

点击查看摘要

Abstract:In this paper, we present a new approach to bridge the domain gap between synthetic and real-world data for un- manned aerial vehicle (UAV)-based perception. Our formu- lation is designed for dynamic scenes, consisting of moving objects or human actions, where the goal is to recognize the pose or actions. We propose an extension of K-Planes Neural Radiance Field (NeRF), wherein our algorithm stores a set of tiered feature vectors. The tiered feature vectors are generated to effectively model conceptual information about a scene as well as an image decoder that transforms output feature maps into RGB images. Our technique leverages the information amongst both static and dynamic objects within a scene and is able to capture salient scene attributes of high altitude videos. We evaluate its performance on challenging datasets, including Okutama Action and UG2, and observe considerable improvement in accuracy over state of the art aerial perception algorithms.

CV-105-标题: Deep Image Restoration For Image Anti-Forensics

链接: https://arxiv.org/abs/2405.02751
作者: Eren Tahir, Mert Bal
备注:

点击查看摘要

Abstract:While image forensics is concerned with whether an image has been tampered with, image anti-forensics attempts to prevent image forensics methods from detecting tampered images. The competition between these two fields started long before the advancement of deep learning. JPEG compression, blurring and noising, which are simple methods by today’s standards, have long been used for anti-forensics and have been the subject of much research in both forensics and anti-forensics. Although these traditional methods are old, they make it difficult to detect fake images and are used for data augmentation in training deep image forgery detection models. In addition to making the image difficult to detect, these methods leave traces on the image and consequently degrade the image quality. Separate image forensics methods have also been developed to detect these traces. In this study, we go one step further and improve the image quality after these methods with deep image restoration models and make it harder to detect the forged image. We evaluate the impact of these methods on image quality. We then test both our proposed methods with deep learning and methods without deep learning on the two best existing image manipulation detection models. In the obtained results, we show how existing image forgery detection models fail against the proposed methods. Code implementation will be publicly available at this https URL .

CV-106-标题: U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformer s

链接: https://arxiv.org/abs/2405.02730
作者: Yuchuan Tian, Zhijun Tu, Hanting Chen, Jie Hu, Chao Xu, Yunhe Wang
备注: 11 pages, 5 figures

点击查看摘要

Abstract:Diffusion Transformers (DiTs) introduce the transformer architecture to diffusion tasks for latent-space image generation. With an isotropic architecture that chains a series of transformer blocks, DiTs demonstrate competitive performance and good scalability; but meanwhile, the abandonment of U-Net by DiTs and their following improvements is worth rethinking. To this end, we conduct a simple toy experiment by comparing a U-Net architectured DiT with an isotropic one. It turns out that the U-Net architecture only gain a slight advantage amid the U-Net inductive bias, indicating potential redundancies within the U-Net-style DiT. Inspired by the discovery that U-Net backbone features are low-frequency-dominated, we perform token downsampling on the query-key-value tuple for self-attention and bring further improvements despite a considerable amount of reduction in computation. Based on self-attention with downsampled tokens, we propose a series of U-shaped DiTs (U-DiTs) in the paper and conduct extensive experiments to demonstrate the extraordinary performance of U-DiT models. The proposed U-DiT could outperform DiT-XL/2 with only 1/6 of its computation cost. Codes are available at this https URL.

CV-107-标题: AFter: Attention-based Fusion Router for RGBT Tracking

链接: https://arxiv.org/abs/2405.02717
作者: Andong Lu, Wanyu Wang, Chenglong Li, Jin Tang, Bin Luo
备注: Peer review

点击查看摘要

Abstract:Multi-modal feature fusion as a core investigative component of RGBT tracking emerges numerous fusion studies in recent years. However, existing RGBT tracking methods widely adopt fixed fusion structures to integrate multi-modal feature, which are hard to handle various challenges in dynamic scenarios. To address this problem, this work presents a novel \emphAttention-based \emphFusion rou\emphter called AFter, which optimizes the fusion structure to adapt to the dynamic challenging scenarios, for robust RGBT tracking. In particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units corresponding to a fusion structure. Through optimizing the combination of attention-based fusion units, we can dynamically select the fusion structure to adapt to various challenging scenarios. Unlike complex search of different structures in neural architecture search algorithms, we develop a dynamic routing algorithm, which equips each attention-based fusion unit with a router, to predict the combination weights for efficient optimization of the fusion structure. Extensive experiments on five mainstream RGBT tracking datasets demonstrate the superior performance of the proposed AFter against state-of-the-art RGBT trackers. We release the code in this https URL.

CV-108-标题: Towards a Scalable Identification of Novel Modes in Generative Models

链接: https://arxiv.org/abs/2405.02700
作者: Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li, Farzan Farnia
备注:

点击查看摘要

Abstract:An interpretable comparison of generative models requires the identification of sample types produced more frequently by each of the involved models. While several quantitative scores have been proposed in the literature to rank different generative models, such score-based evaluations do not reveal the nuanced differences between the generative models in capturing various sample types. In this work, we propose a method called Fourier-based Identification of Novel Clusters (FINC) to identify modes produced by a generative model with a higher frequency in comparison to a reference distribution. FINC provides a scalable stochastic algorithm based on random Fourier features to estimate the eigenspace of kernel covariance matrices of two generative models and utilize the principal eigendirections to detect the sample types present more dominantly in each model. We demonstrate the application of the FINC method to standard computer vision datasets and generative model frameworks. Our numerical results suggest the scalability and efficiency of the developed Fourier-based method in highlighting the sample types captured with different frequencies by widely-used generative models.

CV-109-标题: Stable Diffusion Dataset Generation for Downstream Classification Tasks

链接: https://arxiv.org/abs/2405.02698
作者: Eugenio Lomurno, Matteo D’Oria, Matteo Matteucci
备注:

点击查看摘要

Abstract:Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data. This paper explores the adaptation of the Stable Diffusion 2.0 model for generating synthetic datasets, using Transfer Learning, Fine-Tuning and generation parameter optimisation techniques to improve the utility of the dataset for downstream classification tasks. We present a class-conditional version of the model that exploits a Class-Encoder and optimisation of key generation parameters. Our methodology led to synthetic datasets that, in a third of cases, produced models that outperformed those trained on real datasets.

CV-110-标题: Diffeomorphic Transformer -based Abdomen MRI-CT Deformable Image Registration

链接: https://arxiv.org/abs/2405.02692
作者: Yang Lei, Luke A. Matkovic, Justin Roper, Tonghe Wang, Jun Zhou, Beth Ghavidel, Mark McDonald, Pretesh Patel, Xiaofeng Yang
备注: 18 pages and 4 figures

点击查看摘要

Abstract:This paper aims to create a deep learning framework that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images. The proposed method assumed a diffeomorphic deformation. By using topology-preserved deformation features extracted from the probabilistic diffeomorphic registration model, abdominal motion can be accurately obtained and utilized for DVF estimation. The model integrated Swin transformers, which have demonstrated superior performance in motion tracking, into the convolutional neural network (CNN) for deformation feature extraction. The model was optimized using a cross-modality image similarity loss and a surface matching loss. To compute the image loss, a modality-independent neighborhood descriptor (MIND) was used between the deformed MRI and CT images. The surface matching loss was determined by measuring the distance between the warped coordinates of the surfaces of contoured structures on the MRI and CT images. The deformed MRI image was assessed against the CT image using the target registration error (TRE), Dice similarity coefficient (DSC), and mean surface distance (MSD) between the deformed contours of the MRI image and manual contours of the CT image. When compared to only rigid registration, DIR with the proposed method resulted in an increase of the mean DSC values of the liver and portal vein from 0.850 and 0.628 to 0.903 and 0.763, a decrease of the mean MSD of the liver from 7.216 mm to 3.232 mm, and a decrease of the TRE from 26.238 mm to 8.492 mm. The proposed deformable image registration method based on a diffeomorphic transformer provides an effective and efficient way to generate an accurate DVF from an MRI-CT image pair of the abdomen. It could be utilized in the current treatment planning workflow for liver radiotherapy.

CV-111-标题: Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Images

链接: https://arxiv.org/abs/2405.02686
作者: Yik San Cheng, Runkai Zhao, Heng Wang, Hanchuan Peng, Weidong Cai
备注: 3 pages

点击查看摘要

Abstract:Neuron reconstruction, one of the fundamental tasks in neuroscience, rebuilds neuronal morphology from 3D light microscope imaging data. It plays a critical role in analyzing the structure-function relationship of neurons in the nervous system. However, due to the scarcity of neuron datasets and high-quality SWC annotations, it is still challenging to develop robust segmentation methods for single neuron reconstruction. To address this limitation, we aim to distill the consensus knowledge from massive natural image data to aid the segmentation model in learning the complex neuron structures. Specifically, in this work, we propose a novel training paradigm that leverages a 2D Vision Transformer model pre-trained on large-scale natural images to initialize our Transformer-based 3D neuron segmentation model with a tailored 2D-to-3D weight transferring strategy. Our method builds a knowledge sharing connection between the abundant natural and the scarce neuron image domains to improve the 3D neuron segmentation ability in a data-efficiency manner. Evaluated on a popular benchmark, BigNeuron, our method enhances neuron segmentation performance by 8.71% over the model trained from scratch with the same amount of training samples.

CV-112-标题: Position Paper: Quo Vadis Unsupervised Time Series Anomaly Detection? ICML2024

链接: https://arxiv.org/abs/2405.02678
作者: M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer, Kunyu Peng, Marios Koulakis
备注: ICML 2024

点击查看摘要

Abstract:The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking practices, and a lack of proper justification for the choices made in novel deep learning-based model designs. Our paper presents a critical analysis of the status quo in TAD, revealing the misleading track of current research and highlighting problematic methods, and evaluation practices. Our position advocates for a shift in focus from pursuing only the novelty in model design to improving benchmarking practices, creating non-trivial datasets, and placing renewed emphasis on studying the utility of model architectures for specific tasks. Our findings demonstrate the need for rigorous evaluation protocols, the creation of simple baselines, and the revelation that state-of-the-art deep anomaly detection models effectively learn linear mappings. These findings suggest the need for more exploration and development of simple and interpretable TAD methods. The increment of model complexity in the state-of-the-art deep-learning based models unfortunately offers very little improvement. We offer insights and suggestions for the field to move forward.

CV-113-标题: Hand-Object Interaction Controller (HOIC): Deep Reinforcement Learning for Reconstructing Interactions with Physics SIGGRAPH2024

链接: https://arxiv.org/abs/2405.02676
作者: Haoyu Hu, Xinyu Yi, Zhe Cao, Jun-Hai Yong, Feng Xu
备注: SIGGRAPH 2024 Conference Track

点击查看摘要

Abstract:Hand manipulating objects is an important interaction motion in our daily activities. We faithfully reconstruct this motion with a single RGBD camera by a novel deep reinforcement learning method to leverage physics. Firstly, we propose object compensation control which establishes direct object control to make the network training more stable. Meanwhile, by leveraging the compensation force and torque, we seamlessly upgrade the simple point contact model to a more physical-plausible surface contact model, further improving the reconstruction accuracy and physical correctness. Experiments indicate that without involving any heuristic physical rules, this work still successfully involves physics in the reconstruction of hand-object interactions which are complex motions hard to imitate with deep reinforcement learning. Our code and data are available at this https URL.

CV-114-标题: Deep Pulse-Signal Magnification for remote Heart Rate Estimation in Compressed Videos

链接: https://arxiv.org/abs/2405.02652
作者: Joaquim Comas, Adria Ruiz, Federico Sukno
备注:

点击查看摘要

Abstract:Recent advancements in remote heart rate measurement (rPPG), motivated by data-driven approaches, have significantly improved accuracy. However, certain challenges, such as video compression, still remain: recovering the rPPG signal from highly compressed videos is particularly complex. Although several studies have highlighted the difficulties and impact of video compression for this, effective solutions remain limited. In this paper, we present a novel approach to address the impact of video compression on rPPG estimation, which leverages a pulse-signal magnification transformation to adapt compressed videos to an uncompressed data domain in which the rPPG signal is magnified. We validate the effectiveness of our model by exhaustive evaluations on two publicly available datasets, UCLA-rPPG and UBFC-rPPG, employing both intra- and cross-database performance at several compression rates. Additionally, we assess the robustness of our approach on two additional highly compressed and widely-used datasets, MAHNOB-HCI and COHFACE, which reveal outstanding heart rate estimation results.

CV-115-标题: A Conformal Prediction Score that is Robust to Label Noise

链接: https://arxiv.org/abs/2405.02648
作者: Coby Penso, Jacob Goldberger
备注:

点击查看摘要

Abstract:Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study we tackle the problem of CP calibration based on a validation set with noisy labels. We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. In the test phase the noise-free score is used to form the prediction set. We applied the proposed algorithm to several standard medical imaging classification datasets. We show that our method outperforms current methods by a large margin, in terms of the average size of the prediction set, while maintaining the required coverage.

CV-116-标题: UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model CVPR2024

链接: https://arxiv.org/abs/2405.02608
作者: Shuai Yuan, Lei Luo, Zhuo Hui, Can Pu, Xiaoyu Xiang, Rakesh Ranjan, Denis Demandolx
备注: Accepted by CVPR 2024. Code is available at this https URL

点击查看摘要

Abstract:Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore, we propose UnSAMFlow, an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations, our method produces clear optical flow estimation with sharp boundaries around objects, which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.

CV-117-标题: Vision-based 3D occupancy prediction in autonomous driving: a review and outlook

链接: https://arxiv.org/abs/2405.02595
作者: Yanan Zhang, Jinqing Zhang, Zengran Wang, Junhao Xu, Di Huang
备注: 20 pages, 20 figures

点击查看摘要

Abstract:In recent years, autonomous driving has garnered escalating attention for its potential to relieve drivers’ burdens and improve driving safety. Vision-based 3D occupancy prediction, which predicts the spatial occupancy status and semantics of 3D voxel grids around the autonomous vehicle from image inputs, is an emerging perception task suitable for cost-effective perception system of autonomous driving. Although numerous studies have demonstrated the greater advantages of 3D occupancy prediction over object-centric perception tasks, there is still a lack of a dedicated review focusing on this rapidly developing field. In this paper, we first introduce the background of vision-based 3D occupancy prediction and discuss the challenges in this task. Secondly, we conduct a comprehensive survey of the progress in vision-based 3D occupancy prediction from three aspects: feature enhancement, deployment friendliness and label efficiency, and provide an in-depth analysis of the potentials and challenges of each category of methods. Finally, we present a summary of prevailing research trends and propose some inspiring future outlooks. To provide a valuable reference for researchers, a regularly updated collection of related papers, datasets, and codes is organized at this https URL.

CV-118-标题: Better YOLO with Attention-Augmented Network and Enhanced Generalization Performance for Safety Helmet Detection

链接: https://arxiv.org/abs/2405.02591
作者: Shuqi Shen, Junjie Yang
备注:

点击查看摘要

Abstract:Safety helmets play a crucial role in protecting workers from head injuries in construction sites, where potential hazards are prevalent. However, currently, there is no approach that can simultaneously achieve both model accuracy and performance in complex environments. In this study, we utilized a Yolo-based model for safety helmet detection, achieved a 2% improvement in mAP (mean Average Precision) performance while reducing parameters and Flops count by over 25%. YOLO(You Only Look Once) is a widely used, high-performance, lightweight model architecture that is well suited for complex environments. We presents a novel approach by incorporating a lightweight feature extraction network backbone based on GhostNetv2, integrating attention modules such as Spatial Channel-wise Attention Net(SCNet) and Coordination Attention Net(CANet), and adopting the Gradient Norm Aware optimizer (GAM) for improved generalization ability. In safety-critical environments, the accurate detection and speed of safety helmets plays a pivotal role in preventing occupational hazards and ensuring compliance with safety protocols. This work addresses the pressing need for robust and efficient helmet detection methods, offering a comprehensive framework that not only enhances accuracy but also improves the adaptability of detection models to real-world conditions. Our experimental results underscore the synergistic effects of GhostNetv2, attention modules, and the GAM optimizer, presenting a compelling solution for safety helmet detection that achieves superior performance in terms of accuracy, generalization, and efficiency.

CV-119-标题: Generalizing CLIP to Unseen Domain via Text-Guided Diverse Novel Feature Synthesis

链接: https://arxiv.org/abs/2405.02586
作者: Siyuan Yan, Cheng Luo, Zhen Yu, Zongyuan Ge
备注: 24 pages

点击查看摘要

Abstract:Vision-language foundation models like CLIP have shown impressive zero-shot generalization, but finetuning on downstream datasets can cause overfitting and loss of its generalization ability on unseen domains. Although collecting additional data from new domains of interest is possible, this method is often impractical due to the challenges in obtaining annotated data. To address this, we propose a plug-and-play feature augmentation method called LDFS (Language-Guided Diverse Feature Synthesis) to synthesize new domain features and improve existing CLIP fine-tuning strategies. LDFS has three main contributions: 1) To synthesize novel domain features and promote diversity, we propose an instance-conditional feature augmentation strategy based on a textguided feature augmentation loss. 2) To maintain feature quality after augmenting, we introduce a pairwise regularizer to preserve augmented feature coherence within the CLIP feature space. 3) We propose to use stochastic text feature augmentation to reduce the modality gap and further facilitate the process of text-guided feature synthesis. Extensive experiments show LDFS superiority in improving CLIP generalization ability on unseen domains without collecting data from those domains. The code will be made publicly available.

CV-120-标题: Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements CVPR24

链接: https://arxiv.org/abs/2405.02581
作者: Niccolò Biondi, Federico Pernici, Simone Ricci, Alberto Del Bimbo
备注: Accepted at CVPR24 as Poster Highlight

点击查看摘要

Abstract:Learning compatible representations enables the interchangeable use of semantic features as models are updated over time. This is particularly relevant in search and retrieval systems where it is crucial to avoid reprocessing of the gallery images with the updated model. While recent research has shown promising empirical evidence, there is still a lack of comprehensive theoretical understanding about learning compatible representations. In this paper, we demonstrate that the stationary representations learned by the d -Simplex fixed classifier optimally approximate compatibility representation according to the two inequality constraints of its formal definition. This not only establishes a solid foundation for future works in this line of research but also presents implications that can be exploited in practical learning scenarios. An exemplary application is the now-standard practice of downloading and fine-tuning new pre-trained models. Specifically, we show the strengths and critical issues of stationary representations in the case in which a model undergoing sequential fine-tuning is asynchronously replaced by downloading a better-performing model pre-trained elsewhere. Such a representation enables seamless delivery of retrieval service (i.e., no reprocessing of gallery images) and offers improved performance without operational disruptions during model replacement. Code available at: this https URL.

CV-121-标题: ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy

链接: https://arxiv.org/abs/2405.02571
作者: Soumyadeep Chandra, Sayeed Shafayet Chowdhury, Courtney Yong, Chandru P. Sundaram, Kaushik Roy
备注: Nephrectomy surgery, Surgical Phase Recognition, Surgical Workflow Segmentation, 11 pages, 2 figures, 2 tables

点击查看摘要

Abstract:Surgical action localization is a challenging computer vision problem. While it has promising applications including automated training of surgery procedures, surgical workflow optimization, etc., appropriate model design is pivotal to accomplishing this task. Moreover, the lack of suitable medical datasets adds an additional layer of complexity. To that effect, we introduce a new complex dataset of nephrectomy surgeries called UroSlice. To perform the action localization from these videos, we propose a novel model termed as `ViTALS’ (Vision Transformer for Action Localization in Surgical Nephrectomy). Our model incorporates hierarchical dilated temporal convolution layers and inter-layer residual connections to capture the temporal correlations at finer as well as coarser granularities. The proposed approach achieves state-of-the-art performance on Cholec80 and UroSlice datasets (89.8% and 66.1% accuracy, respectively), validating its effectiveness.

CV-122-标题: ActiveNeuS: Active 3D Reconstruction using Neural Implicit Surface Uncertainty

链接: https://arxiv.org/abs/2405.02568
作者: Hyunseo Kim, Hyeonseo Yang, Taekyung Kim, YoonSung Kim, Jin-Hwa Kim, Byoung-Tak Zhang
备注:

点击查看摘要

Abstract:Active learning in 3D scene reconstruction has been widely studied, as selecting informative training views is critical for the reconstruction. Recently, Neural Radiance Fields (NeRF) variants have shown performance increases in active 3D reconstruction using image rendering or geometric uncertainty. However, the simultaneous consideration of both uncertainties in selecting informative views remains unexplored, while utilizing different types of uncertainty can reduce the bias that arises in the early training stage with sparse inputs. In this paper, we propose ActiveNeuS, which evaluates candidate views considering both uncertainties. ActiveNeuS provides a way to accumulate image rendering uncertainty while avoiding the bias that the estimated densities can introduce. ActiveNeuS computes the neural implicit surface uncertainty, providing the color uncertainty along with the surface information. It efficiently handles the bias by using the surface information and a grid, enabling the fast selection of diverse viewpoints. Our method outperforms previous works on popular datasets, Blender and DTU, showing that the views selected by ActiveNeuS significantly improve performance.

CV-123-标题: Leveraging the Human Ventral Visual Stream to Improve Neural Network Robustness

链接: https://arxiv.org/abs/2405.02564
作者: Zhenan Shao, Linjian Ma, Bo Li, Diane M. Beck
备注:

点击查看摘要

Abstract:Human object recognition exhibits remarkable resilience in cluttered and dynamic visual environments. In contrast, despite their unparalleled performance across numerous visual tasks, Deep Neural Networks (DNNs) remain far less robust than humans, showing, for example, a surprising susceptibility to adversarial attacks involving image perturbations that are (almost) imperceptible to humans. Human object recognition likely owes its robustness, in part, to the increasingly resilient representations that emerge along the hierarchy of the ventral visual cortex. Here we show that DNNs, when guided by neural representations from a hierarchical sequence of regions in the human ventral visual stream, display increasing robustness to adversarial attacks. These neural-guided models also exhibit a gradual shift towards more human-like decision-making patterns and develop hierarchically smoother decision surfaces. Importantly, the resulting representational spaces differ in important ways from those produced by conventional smoothing methods, suggesting that such neural-guidance may provide previously unexplored robustness solutions. Our findings support the gradual emergence of human robustness along the ventral visual hierarchy and suggest that the key to DNN robustness may lie in increasing emulation of the human brain.

CV-124-标题: Few-Shot Fruit Segmentation via Transfer Learning ICRA

链接: https://arxiv.org/abs/2405.02556
作者: Jordan A. James, Heather K. Manching, Amanda M. Hulse-Kemp, William J. Beksi
备注: To be published in the 2024 IEEE International Conference on Robotics and Automation (ICRA)

点击查看摘要

Abstract:Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion. In this paper, we develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Concretely, our work is aimed at addressing agricultural domains that lack publicly available labeled data. Motivated by similar success in urban scene parsing, we propose specialized pre-training using a public benchmark dataset for fruit transfer learning. By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images. Furthermore, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset.

CV-125-标题: AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition

链接: https://arxiv.org/abs/2405.02538
作者: Meiqi Cao, Rui Yan, Xiangbo Shu, Guangzhao Dai, Yazhou Yao, Guo-Sen Xie
备注:

点击查看摘要

Abstract:Panoramic Activity Recognition (PAR) aims to identify multi-granularity behaviors performed by multiple persons in panoramic scenes, including individual activities, group activities, and global activities. Previous methods 1) heavily rely on manually annotated detection boxes in training and inference, hindering further practical deployment; or 2) directly employ normal detectors to detect multiple persons with varying size and spatial occlusion in panoramic scenes, blocking the performance gain of PAR. To this end, we consider learning a detector adapting varying-size occluded persons, which is optimized along with the recognition module in the all-in-one framework. Therefore, we propose a novel Adapt-Focused bi-Propagating Prototype learning (AdaFPP) framework to jointly recognize individual, group, and global activities in panoramic activity scenes by learning an adapt-focused detector and multi-granularity prototypes as the pretext tasks in an end-to-end way. Specifically, to accommodate the varying sizes and spatial occlusion of multiple persons in crowed panoramic scenes, we introduce a panoramic adapt-focuser, achieving the size-adapting detection of individuals by comprehensively selecting and performing fine-grained detections on object-dense sub-regions identified through original detections. In addition, to mitigate information loss due to inaccurate individual localizations, we introduce a bi-propagation prototyper that promotes closed-loop interaction and informative consistency across different granularities by facilitating bidirectional information propagation among the individual, group, and global levels. Extensive experiments demonstrate the significant performance of AdaFPP and emphasize its powerful applicability for PAR.

CV-126-标题: SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap

链接: https://arxiv.org/abs/2405.02515
作者: Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
备注: MLMI2023

点击查看摘要

Abstract:Computed tomography (CT) is a widely used non-invasive medical imaging technique for disease diagnosis. The diagnostic accuracy is often affected by image resolution, which can be insufficient in practice. For medical CT images, the through-plane resolution is often worse than the in-plane resolution and there can be overlap between slices, causing difficulties in diagnoses. Self-supervised methods for through-plane resolution enhancement, which train on in-plane images and infer on through-plane images, have shown promise for both CT and MRI imaging. However, existing self-supervised methods either neglect overlap or can only handle specific cases with fixed combinations of resolution and overlap. To address these limitations, we propose a self-supervised method called SR4ZCT. It employs the same off-axis training approach while being capable of handling arbitrary combinations of resolution and overlap. Our method explicitly models the relationship between resolutions and voxel spacings of different planes to accurately simulate training images that match the original through-plane images. We highlight the significance of accurate modeling in self-supervised off-axis training and demonstrate the effectiveness of SR4ZCT using a real-world dataset.

CV-127-标题: Spatio-Temporal SwinMAE: A Swin Transformer based Multiscale Representation Learner for Temporal Satellite Imagery

链接: https://arxiv.org/abs/2405.02512
作者: Yohei Nakayama, Jiawei Su
备注:

点击查看摘要

Abstract:Currently, the foundation models represented by large language models have made dramatic progress and are used in a very wide range of domains including 2D and 3D vision. As one of the important application domains of foundation models, earth observation has attracted attention and various approaches have been developed. When considering earth observation as a single image capture, earth observation imagery can be processed as an image with three or more channels, and when it comes with multiple image captures of different timestamps at one location, the temporal observation can be considered as a set of continuous image resembling video frames or medical SCAN slices. This paper presents Spatio-Temporal SwinMAE (ST-SwinMAE), an architecture which particularly focuses on representation learning for spatio-temporal image processing. Specifically, it uses a hierarchical Masked Auto-encoder (MAE) with Video Swin Transformer blocks. With the architecture, we present a pretrained model named Degas 100M as a geospatial foundation model. Also, we propose an approach for transfer learning with Degas 100M, which both pretrained encoder and decoder of MAE are utilized with skip connections added between them to achieve multi-scale information communication, forms an architecture named Spatio-Temporal SwinUNet (ST-SwinUNet). Our approach shows significant improvements of performance over existing state-of-the-art of foundation models. Specifically, for transfer learning of the land cover downstream task on the PhilEO Bench dataset, it shows 10.4% higher accuracy compared with other geospatial foundation models on average.

CV-128-标题: Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction

链接: https://arxiv.org/abs/2405.02509
作者: Jiayang Shi, Junyi Zhu, Daniel M. Pelt, K. Joost Batenburg, Matthew B. Blaschko
备注:

点击查看摘要

Abstract:Computed Tomography (CT) is pivotal in industrial quality control and medical diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces challenges due to its under-sampled nature, leading to ill-posed reconstruction problems. Recent advancements in Implicit Neural Representations (INRs) have shown promise in addressing sparse-view CT reconstruction. Recognizing that CT often involves scanning similar subjects, we propose a novel approach to improve reconstruction quality through joint reconstruction of multiple objects using INRs. This approach can potentially leverage both the strengths of INRs and the statistical regularities across multiple objects. While current INR joint reconstruction techniques primarily focus on accelerating convergence via meta-initialization, they are not specifically tailored to enhance reconstruction quality. To address this gap, we introduce a novel INR-based Bayesian framework integrating latent variables to capture the inter-object relationships. These variables serve as a dynamic reference throughout the optimization, thereby enhancing individual reconstruction fidelity. Our extensive experiments, which assess various key factors such as reconstruction quality, resistance to overfitting, and generalizability, demonstrate significant improvements over baselines in common numerical metrics. This underscores a notable advancement in CT reconstruction methods.

CV-129-标题: Rasterized Edge Gradients: Handling Discontinuities Differentiably

链接: https://arxiv.org/abs/2405.02508
作者: Stanislav Pidhorskyi, Tomas Simon, Gabriel Schwartz, He Wen, Yaser Sheikh, Jason Saragih
备注:

点击查看摘要

Abstract:Computing the gradients of a rendering process is paramount for diverse applications in computer vision and graphics. However, accurate computation of these gradients is challenging due to discontinuities and rendering approximations, particularly for surface-based representations and rasterization-based rendering. We present a novel method for computing gradients at visibility discontinuities for rasterization-based differentiable renderers. Our method elegantly simplifies the traditionally complex problem through a carefully designed approximation strategy, allowing for a straightforward, effective, and performant solution. We introduce a novel concept of micro-edges, which allows us to treat the rasterized images as outcomes of a differentiable, continuous process aligned with the inherently non-differentiable, discrete-pixel rasterization. This technique eliminates the necessity for rendering approximations or other modifications to the forward pass, preserving the integrity of the rendered image, which makes it applicable to rasterized masks, depth, and normals images where filtering is prohibitive. Utilizing micro-edges simplifies gradient interpretation at discontinuities and enables handling of geometry intersections, offering an advantage over the prior art. We showcase our method in dynamic human head scene reconstruction, demonstrating effective handling of camera images and segmentation masks.

CV-130-标题: Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids SIGGRAPH2024

链接: https://arxiv.org/abs/2405.02386
作者: Junchen Liu, Wenbo Hu, Zhuo Yang, Jianteng Chen, Guoliang Wang, Xiaoxue Chen, Yantong Cai, Huan-ang Gao, Hao Zhao
备注: SIGGRAPH 2024, Project page: this https URL , Code: this https URL

点击查看摘要

Abstract:Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge to effectively and efficiently characterize anisotropic areas induced by the cone-casting procedure. This paper introduces a Ripmap-Encoded Platonic Solid representation to precisely and efficiently featurize 3D anisotropic areas, achieving high-fidelity anti-aliasing renderings. Central to our approach are two key components: Platonic Solid Projection and Ripmap encoding. The Platonic Solid Projection factorizes the 3D space onto the unparalleled faces of a certain Platonic solid, such that the anisotropic 3D areas can be projected onto planes with distinguishable characterization. Meanwhile, each face of the Platonic solid is encoded by the Ripmap encoding, which is constructed by anisotropically pre-filtering a learnable feature grid, to enable featurzing the projected anisotropic areas both precisely and efficiently by the anisotropic area-sampling. Extensive experiments on both well-established synthetic datasets and a newly captured real-world dataset demonstrate that our Rip-NeRF attains state-of-the-art rendering quality, particularly excelling in the fine details of repetitive structures and textures, while maintaining relatively swift training times.

CV-131-标题: Enhancing Social Media Post Popularity Prediction with Visual Content

链接: https://arxiv.org/abs/2405.02367
作者: Dahyun Jeong, Hyelim Son, Yunjin Choi, Keunwoo Kim
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点击查看摘要

Abstract:Our study presents a framework for predicting image-based social media content popularity that focuses on addressing complex image information and a hierarchical data structure. We utilize the Google Cloud Vision API to effectively extract key image and color information from users’ postings, achieving 6.8% higher accuracy compared to using non-image covariates alone. For prediction, we explore a wide range of prediction models, including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, with linear regression as the benchmark. Our comparative study demonstrates that models that are capable of capturing the underlying nonlinear interactions between covariates outperform other methods.

CV-132-标题: Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics

链接: https://arxiv.org/abs/2405.02334
作者: Francesco Prinzi, Carmelo Militello, Calogero Zarcaro, Tommaso Vincenzo Bartolotta, Salvatore Gaglio, Salvatore Vitabile
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点击查看摘要

Abstract:In the last years, artificial intelligence (AI) in clinical decision support systems (CDSS) played a key role in harnessing machine learning and deep learning architectures. Despite their promising capabilities, the lack of transparency and explainability of AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. Achieving transparency without compromising predictive accuracy remains a key challenge. This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN-derived features with the interpretability inherent in radiomic features. Rad4XCNN diverges from conventional methods based on saliency map, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Using a breast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. Some key results are: i) CNN-derived features guarantee more robust accuracy when compared against ViT-derived and radiomic features; ii) conventional visualization map methods for explanation present several pitfalls; iii) Rad4XCNN does not sacrifice model accuracy for their explainability; iv) Rad4XCNN provides global explanation insights enabling the physician to analyze the model outputs and findings. In addition, we highlight the importance of integrating interpretability into AI models for enhanced trust and adoption in clinical practice, emphasizing how our method can mitigate some concerns related to explainable AI methods.

CV-133-标题: Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models

链接: https://arxiv.org/abs/2405.02332
作者: Adrien Le Coz, Houssem Ouertatani, Stéphane Herbin, Faouzi Adjed
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点击查看摘要

Abstract:Image classifiers should be used with caution in the real world. Performance evaluated on a validation set may not reflect performance in the real world. In particular, classifiers may perform well for conditions that are frequently encountered during training, but poorly for other infrequent conditions. In this study, we hypothesize that recent advances in text-to-image generative models make them valuable for benchmarking computer vision models such as image classifiers: they can generate images conditioned by textual prompts that cause classifier failures, allowing failure conditions to be described with textual attributes. However, their generation cost becomes an issue when a large number of synthetic images need to be generated, which is the case when many different attribute combinations need to be tested. We propose an image classifier benchmarking method as an iterative process that alternates image generation, classifier evaluation, and attribute selection. This method efficiently explores the attributes that ultimately lead to poor behavior detection.

CV-134-标题: Long-term Human Participation Assessment In Collaborative Learning Environments Using Dynamic Scene Analysis

链接: https://arxiv.org/abs/2405.02317
作者: Wenjing Shi, Phuong Tran, Sylvia Celedón-Pattichis, Marios S. Pattichis
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点击查看摘要

Abstract:The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state of the art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos.

CV-135-标题: YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count

链接: https://arxiv.org/abs/2405.02312
作者: Mahmudul Islam Masum, Arif Sarwat, Hugo Riggs, Alicia Boymelgreen, Preyojon Dey
备注: 12 pages, 25 figures

点击查看摘要

Abstract:This paper presents a comparative study of object detection using YOLOv5 and YOLOv8 for three distinct classes: artemia, cyst, and excrement. In this comparative study, we analyze the performance of these models in terms of accuracy, precision, recall, etc. where YOLOv5 often performed better in detecting Artemia and cysts with excellent precision and accuracy. However, when it came to detecting excrement, YOLOv5 faced notable challenges and limitations. This suggests that YOLOv8 offers greater versatility and adaptability in detection tasks while YOLOv5 may struggle in difficult situations and may need further fine-tuning or specialized training to enhance its performance. The results show insights into the suitability of YOLOv5 and YOLOv8 for detecting objects in challenging marine environments, with implications for applications such as ecological research.

CV-136-标题: TFCounter:Polishing Gems for Training-Free Object Counting

链接: https://arxiv.org/abs/2405.02301
作者: Pan Ting, Jianfeng Lin, Wenhao Yu, Wenlong Zhang, Xiaoying Chen, Jinlu Zhang, Binqiang Huang
备注: 14pages,11 figuers

点击查看摘要

Abstract:Object counting is a challenging task with broad application prospects in security surveillance, traffic management, and disease diagnosis. Existing object counting methods face a tri-fold challenge: achieving superior performance, maintaining high generalizability, and minimizing annotation costs. We develop a novel training-free class-agnostic object counter, TFCounter, which is prompt-context-aware via the cascade of the essential elements in large-scale foundation models. This approach employs an iterative counting framework with a dual prompt system to recognize a broader spectrum of objects varying in shape, appearance, and size. Besides, it introduces an innovative context-aware similarity module incorporating background context to enhance accuracy within messy scenes. To demonstrate cross-domain generalizability, we collect a novel counting dataset named BIKE-1000, including exclusive 1000 images of shared bicycles from Meituan. Extensive experiments on FSC-147, CARPK, and BIKE-1000 datasets demonstrate that TFCounter outperforms existing leading training-free methods and exhibits competitive results compared to trained counterparts.

CV-137-标题: Employing Universal Voting Schemes for Improved Visual Place Recognition Performance

链接: https://arxiv.org/abs/2405.02297
作者: Maria Waheed, Michael Milford, Xiaojun Zhai, Maria Fasli, Klaus McDonald-Maier, Shoaib Ehsan
备注: arXiv admin note: substantial text overlap with arXiv:2305.05705

点击查看摘要

Abstract:Visual Place Recognition has been the subject of many endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion, Fly-Inspired Voting Units, SwitchHit or Switch-Fuse involve combining different VPR techniques together, utilizing different strategies. However, a major aspect often common to many of these strategies is voting. Voting is an extremely relevant topic to explore in terms of its application and significance for any ensemble VPR setup. This paper analyses several voting schemes to maximise the place detection accuracy of a VPR ensemble set up and determine the optimal voting schemes for selection. We take inspiration from a variety of voting schemes that are widely employed in fields such as politics and sociology and it is evident via empirical data that the selection of the voting method influences the results drastically. The paper tests a wide variety of voting schemes to present the improvement in the VPR results for several data sets. We aim to determine whether a single optimal voting scheme exists or, much like in other fields of research, the selection of a voting technique is relative to its application and environment. We propose a ranking of these different voting methods from best to worst which allows for better selection. While presenting our results in terms of voting method’s performance bounds, in form of radar charts, PR curves to showcase the difference in performance and a comparison methodology using a McNemar test variant to determine the statistical significance of the differences. This test is performed to further confirm the reliability of outcomes and draw comparisons for better and informed selection a voting technique.

CV-138-标题: Möbius Transform for Mitigating Perspective Distortions in Representation Learning

链接: https://arxiv.org/abs/2405.02296
作者: Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Marcus Liwicki, Mubarak Shah
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点击查看摘要

Abstract:Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating camera intrinsic and extrinsic parameters is a challenging task that prevents synthesizing perspective distortion. Non-availability of dedicated training data poses a critical barrier to developing robust computer vision methods. Additionally, distortion correction methods make other computer vision tasks a multi-step approach and lack performance. In this work, we propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of Möbius transform to model real-world distortion without estimating camera intrinsic and extrinsic parameters and without the need for actual distorted data. Also, we present a dedicated perspectively distorted benchmark dataset, ImageNet-PD, to benchmark the robustness of deep learning models against this new dataset. The proposed method outperforms on existing benchmarks, ImageNet-E and ImageNet-X. Additionally, it significantly improves performance on ImageNet-PD while consistently performing on standard data distribution. Further, our method shows improved performance on three PD-affected real-world applications: crowd counting, fisheye image recognition, and person re-identification. We will release source code, dataset, and models for foster further research.

CV-139-标题: Neural Additive Image Model: Interpretation through Interpolation

链接: https://arxiv.org/abs/2405.02295
作者: Arik Reuter, Anton Thielmann, Benjamin Saefken
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点击查看摘要

Abstract:Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predictions are highly difficult yet extremely interesting problems. By adopting a holistic modeling approach utilizing Neural Additive Models in combination with Diffusion Autoencoders, we can effectively identify the latent hidden semantics of image effects and achieve full intelligibility of additional tabular effects. Our approach offers a high degree of flexibility, empowering us to comprehensively explore the impact of various image characteristics. We demonstrate that the proposed method can precisely identify complex image effects in an ablation study. To further showcase the practical applicability of our proposed model, we conduct a case study in which we investigate how the distinctive features and attributes captured within host images exert influence on the pricing of Airbnb rentals.

CV-140-标题: Prospective Role of Foundation Models in Advancing Autonomous Vehicles

链接: https://arxiv.org/abs/2405.02288
作者: Jianhua Wu, Bingzhao Gao, Jincheng Gao, Jianhao Yu, Hongqing Chu, Qiankun Yu, Xun Gong, Yi Chang, H. Eric Tseng, Hong Chen, Jie Chen
备注: 36 pages,5 figures

点击查看摘要

Abstract:With the development of artificial intelligence and breakthroughs in deep learning, large-scale Foundation Models (FMs), such as GPT, CLIP, etc., have achieved remarkable results in many fields including natural language processing and computer vision. The application of FMs in autonomous driving holds considerable promise. For example, they can contribute to enhance scene understanding and reasoning. By pre-training on rich linguistic and visual data, FMs can understand and interpret various elements in a driving scene, and provide cognitive reasoning to give linguistic and action commands for driving decisions and planning. Furthermore, FMs can augment data based on its understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data collection. The enhancement can subsequently lead to the improvement in the accuracy and reliability of autonomous driving systems. Another testament to the potential of FMs applications lies in the development of World Models, exemplified by the DREAMER series, which showcase the ability to comprehend physical laws and dynamics. Learning from massive data under the paradigm of self-supervised learning, World Model can generate unseen yet plausible driving environment, facilitating the enhancement in the prediction of road users behavior and the off-line training of driving strategies. In this paper, we synthesize the applications and future trends of FMs in autonomous driving. By utilizing the powerful capabilities of FMs, we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving, consequently advancing overall safety in this domain.

CV-141-标题: An Image Quality Evaluation and Masking Algorithm Based On Pre-trained Deep Neural Networks

链接: https://arxiv.org/abs/2405.03408
作者: Peng Jia, Yu Song, Jiameng Lv, Runyu Ning
备注: Accepted by the AJ. The code could be downloaded from: this https URL with DOI of: 10.12149/101415

点击查看摘要

Abstract:With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluates image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point spread function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep learning based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

CV-142-标题: Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation

链接: https://arxiv.org/abs/2405.03141
作者: Yihao Zhou, Timothy Tin-Yan Lee, Kelly Ka-Lee Lai, Chonglin Wu, Hin Ting Lau, De Yang, Chui-Yi Chan, Winnie Chiu-Wing Chu, Jack Chun-Yiu Cheng, Tsz-Ping Lam, Yong-Ping Zheng
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点击查看摘要

Abstract:The current clinical gold standard for evaluating adolescent idiopathic scoliosis (AIS) is X-ray radiography, using Cobb angle measurement. However, the frequent monitoring of the AIS progression using X-rays poses a challenge due to the cumulative radiation exposure. Although 3D ultrasound has been validated as a reliable and radiation-free alternative for scoliosis assessment, the process of measuring spinal curvature is still carried out manually. Consequently, there is a considerable demand for a fully automatic system that can locate bony landmarks and perform angle measurements. To this end, we introduce an estimation model for automatic ultrasound curve angle (UCA) measurement. The model employs a dual-branch network to detect candidate landmarks and perform vertebra segmentation on ultrasound coronal images. An affinity clustering strategy is utilized within the vertebral segmentation area to illustrate the affinity relationship between candidate landmarks. Subsequently, we can efficiently perform line delineation from a clustered affinity map for UCA measurement. As our method is specifically designed for UCA calculation, this method outperforms other state-of-the-art methods for landmark and line detection tasks. The high correlation between the automatic UCA and Cobb angle (R ^2 =0.858) suggests that our proposed method can potentially replace manual UCA measurement in ultrasound scoliosis assessment.

CV-143-标题: DVMSR: Distillated Vision Mamba for Efficient Super-Resolution

链接: https://arxiv.org/abs/2405.03008
作者: Xiaoyan Lei, Wenlong ZHang, Weifeng Cao
备注: 8 pages, 8 figures

点击查看摘要

Abstract:Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity, which have shown impressive performance on high-level vision tasks. In this paper, we propose DVMSR, a novel lightweight Image SR network that incorporates Vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution, multiple stacked Residual State Space Blocks (RSSBs), and a reconstruction module. Specifically, the deep feature extraction module is composed of several residual state space blocks (RSSB), each of which has several Vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance, we employ a distillation strategy to the vision Mamba network for superior performance. Specifically, we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at this https URL

CV-144-标题: Design analysis and manufacturing of a glass-plastic hybrid minimalist aspheric panoramic annular lens

链接: https://arxiv.org/abs/2405.02942
作者: Shaohua Gao, Qi Jiang, Yiqi Liao, Yi Qiu, Wanglei Ying, Kailun Yang, Kaiwei Wang, Benhao Zhang, Jian Bai
备注: Accepted to Optics & Laser Technology

点击查看摘要

Abstract:We propose a high-performance glass-plastic hybrid minimalist aspheric panoramic annular lens (ASPAL) to solve several major limitations of the traditional panoramic annular lens (PAL), such as large size, high weight, and complex system. The field of view (FoV) of the ASPAL is 360°x(35°~110°) and the imaging quality is close to the diffraction limit. This large FoV ASPAL is composed of only 4 lenses. Moreover, we establish a physical structure model of PAL using the ray tracing method and study the influence of its physical parameters on compactness ratio. In addition, for the evaluation of local tolerances of annular surfaces, we propose a tolerance analysis method suitable for ASPAL. This analytical method can effectively analyze surface irregularities on annular surfaces and provide clear guidance on manufacturing tolerances for ASPAL. Benefiting from high-precision glass molding and injection molding aspheric lens manufacturing techniques, we finally manufactured 20 ASPALs in small batches. The weight of an ASPAL prototype is only 8.5 g. Our framework provides promising insights for the application of panoramic systems in space and weight-constrained environmental sensing scenarios such as intelligent security, micro-UAVs, and micro-robots.

CV-145-标题: I3Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis

链接: https://arxiv.org/abs/2405.02857
作者: Haofei Song, Xintian Mao, Jing Yu, Qingli Li, Yan Wang
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点击查看摘要

Abstract:Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low through-plane resolution. We reveal an intriguing phenomenon that due to the mentioned nature of data, performing slice-wise interpolation from the axial view can yield greater benefits than performing super-resolution from other views. Based on this observation, we propose an Inter-Intra-slice Interpolation Network (I ^3 Net), which fully explores information from high in-plane resolution and compensates for low through-plane resolution. The through-plane branch supplements the limited information contained in low through-plane resolution from high in-plane resolution and enables continual and diverse feature learning. In-plane branch transforms features to the frequency domain and enforces an equal learning opportunity for all frequency bands in a global context learning paradigm. We further propose a cross-view block to take advantage of the information from all three views online. Extensive experiments on two public datasets demonstrate the effectiveness of I ^3 Net, and noticeably outperforms state-of-the-art super-resolution, video frame interpolation and slice interpolation methods by a large margin. We achieve 43.90dB in PSNR, with at least 1.14dB improvement under the upscale factor of \times 2 on MSD dataset with faster inference. Code is available at this https URL.

CV-146-标题: On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks

链接: https://arxiv.org/abs/2405.02852
作者: Fadillah Maani, Anees Ur Rehman Hashmi, Numan Saeed, Mohammad Yaqub
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点击查看摘要

Abstract:Brain tumor segmentation is a fundamental step in assessing a patient’s cancer progression. However, manual segmentation demands significant expert time to identify tumors in 3D multimodal brain MRI scans accurately. This reliance on manual segmentation makes the process prone to intra- and inter-observer variability. This work proposes a brain tumor segmentation method as part of the BraTS-GoAT challenge. The task is to segment tumors in brain MRI scans automatically from various populations, such as adults, pediatrics, and underserved sub-Saharan Africa. We employ a recent CNN architecture for medical image segmentation, namely MedNeXt, as our baseline, and we implement extensive model ensembling and postprocessing for inference. Our experiments show that our method performs well on the unseen validation set with an average DSC of 85.54% and HD95 of 27.88. The code is available on this https URL.

CV-147-标题: MR- Transformer : Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging

链接: https://arxiv.org/abs/2405.02784
作者: Chaojie Zhang, Shengjia Chen, Ozkan Cigdem, Haresh Rengaraj Rajamohan, Kyunghyun Cho, Richard Kijowski, Cem M. Deniz
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点击查看摘要

Abstract:A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates the ImageNet pre-training and captures three-dimensional (3D) spatial correlation from the MR images. The performance of the proposed model was compared to existing state-of-the-art deep learning models for knee injury diagnosis using MRI. Knee MR scans of four different tissue contrasts from the Osteoarthritis Initiative and Multicenter Osteoarthritis Study databases were utilized in the study. Experimental results demonstrated the state-of-the-art performance of the proposed model on TKR prediction using MRI.

CV-148-标题: Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI

链接: https://arxiv.org/abs/2405.02504
作者: Minhui Yu, Mengqi Wu, Ling Yue, Andrea Bozoki, Mingxia Liu
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点击查看摘要

Abstract:Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used in multimodal analysis of neurodegenerative disorders. While MRI is broadly utilized in clinical settings, PET is less accessible. Many studies have attempted to use deep generative models to synthesize PET from MRI scans. However, they often suffer from unstable training and inadequately preserve brain functional information conveyed by PET. To this end, we propose a functional imaging constrained diffusion (FICD) framework for 3D brain PET image synthesis with paired structural MRI as input condition, through a new constrained diffusion model (CDM). The FICD introduces noise to PET and then progressively removes it with CDM, ensuring high output fidelity throughout a stable training phase. The CDM learns to predict denoised PET with a functional imaging constraint introduced to ensure voxel-wise alignment between each denoised PET and its ground truth. Quantitative and qualitative analyses conducted on 293 subjects with paired T1-weighted MRI and 18F-fluorodeoxyglucose (FDG)-PET scans suggest that FICD achieves superior performance in generating FDG-PET data compared to state-of-the-art methods. We further validate the effectiveness of the proposed FICD on data from a total of 1,262 subjects through three downstream tasks, with experimental results suggesting its utility and generalizability.

CV-149-标题: Prediction techniques for dynamic imaging with online primal-dual methods

链接: https://arxiv.org/abs/2405.02497
作者: Neil Dizon, Jyrki Jauhiainen, Tuomo Valkonen
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点击查看摘要

Abstract:Online optimisation facilitates the solution of dynamic inverse problems, such as image stabilisation, fluid flow monitoring, and dynamic medical imaging. In this paper, we improve upon previous work on predictive online primal-dual methods on two fronts. Firstly, we provide a more concise analysis that symmetrises previously unsymmetric regret bounds, and relaxes previous restrictive conditions on the dual predictor. Secondly, based on the latter, we develop several improved dual predictors. We numerically demonstrate their efficacy in image stabilisation and dynamic positron emission tomography.

CV-150-标题: A Fresh Look at Sanity Checks for Saliency Maps

链接: https://arxiv.org/abs/2405.02383
作者: Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina Höhne
备注: arXiv admin note: text overlap with arXiv:2401.06465

点击查看摘要

Abstract:The Model Parameter Randomisation Test (MPRT) is highly recognised in the eXplainable Artificial Intelligence (XAI) community due to its fundamental evaluative criterion: explanations should be sensitive to the parameters of the model they seek to explain. However, recent studies have raised several methodological concerns for the empirical interpretation of MPRT. In response, we propose two modifications to the original test: Smooth MPRT and Efficient MPRT. The former reduces the impact of noise on evaluation outcomes via sampling, while the latter avoids the need for biased similarity measurements by re-interpreting the test through the increase in explanation complexity after full model randomisation. Our experiments show that these modifications enhance the metric reliability, facilitating a more trustworthy deployment of explanation methods.

信息检索

IR-0-标题: ID-centric Pre-training for Recommendation

链接: https://arxiv.org/abs/2405.03562
作者: Yiqing Wu, Ruobing Xie, Zhao Zhang, Fuzhen Zhuang, Xu Zhang, Leyu Lin, Zhanhui Kang, Yongjun Xu
备注:

点击查看摘要

Abstract:Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of pre-trained language model (PLM), some pioneer works adopt PLM for pre-trained recommendation, where modality information (e.g., text) is considered universal across domains via PLM. Unfortunately, the behavioral information in ID embeddings is still verified to be dominating in PLM-based recommendation models compared to modality information and thus limits these models’ performance. In this work, we propose a novel ID-centric recommendation pre-training paradigm (IDP), which directly transfers informative ID embeddings learned in pre-training domains to item representations in new domains. Specifically, in pre-training stage, besides the ID-based sequential model for recommendation, we also build a Cross-domain ID-matcher (CDIM) learned by both behavioral and modality information. In the tuning stage, modality information of new domain items is regarded as a cross-domain bridge built by CDIM. We first leverage the textual information of downstream domain items to retrieve behaviorally and semantically similar items from pre-training domains using CDIM. Next, these retrieved pre-trained ID embeddings, rather than certain textual embeddings, are directly adopted to generate downstream new items’ embeddings. Through extensive experiments on real-world datasets, both in cold and warm settings, we demonstrate that our proposed model significantly outperforms all baselines. Codes will be released upon acceptance.

IR-1-标题: Doing Personal LAPS: LLM -Augmented Dialogue Construction for Personalized Multi-Session Conversational Search SIGIR2024

链接: https://arxiv.org/abs/2405.03480
作者: Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. de Vries, Jeff Dalton, Faegheh Hasibi
备注: Accepted at SIGIR 2024 (Full Paper)

点击查看摘要

Abstract:The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods. The collected dataset is suited to train preference extraction and personalized response generation. Our results show that responses generated explicitly using extracted preferences better match user’s actual preferences, highlighting the value of using extracted preferences over simple dialogue history. Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods.

IR-2-标题: Improving (Re-)Usability of Musical Dataset s: An Overview of the DOREMUS Project

链接: https://arxiv.org/abs/2405.03382
作者: Pasquale Lisena, Manel Achichi (WEB3), Pierre Choffé (BnF), Cécile Cecconi, Konstantin Todorov (WEB3), Bernard Jacquemin (GERIICO), Raphaël Troncy
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点击查看摘要

Abstract:DOREMUS works on a better description of music by building new tools to link and explore the data of three French institutions. This paper gives an overview of the data model based on FRBRoo, explains the conversion and linking processes using linked data technologies and presents the prototypes created to consume the data according to the web users’ needs.

IR-3-标题: Explainability for Transparent Conversational Information-Seeking SIGIR SIGIR’24

链接: https://arxiv.org/abs/2405.03303
作者: Weronika Łajewska, Damiano Spina, Johanne Trippas, Krisztian Balog
备注: This is the author’s version of the work. The definitive version is published in: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), July 14-18, 2024, Washington, DC, USA

点击查看摘要

Abstract:The increasing reliance on digital information necessitates advancements in conversational search systems, particularly in terms of information transparency. While prior research in conversational information-seeking has concentrated on improving retrieval techniques, the challenge remains in generating responses useful from a user perspective. This study explores different methods of explaining the responses, hypothesizing that transparency about the source of the information, system confidence, and limitations can enhance users’ ability to objectively assess the response. By exploring transparency across explanation type, quality, and presentation mode, this research aims to bridge the gap between system-generated responses and responses verifiable by the user. We design a user study to answer questions concerning the impact of (1) the quality of explanations enhancing the response on its usefulness and (2) ways of presenting explanations to users. The analysis of the collected data reveals lower user ratings for noisy explanations, although these scores seem insensitive to the quality of the response. Inconclusive results on the explanations presentation format suggest that it may not be a critical factor in this setting.

IR-4-标题: Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory

链接: https://arxiv.org/abs/2405.03267
作者: Rongxin Cheng, Yifan Peng, Xingda Wei, Hongrui Xie, Rong Chen, Sijie Shen, Haibo Chen
备注:

点击查看摘要

Abstract:Vector searches on large-scale datasets are critical to modern online services like web search and RAG, which necessity storing the datasets and their index on the secondary storage like SSD. In this paper, we are the first to characterize the trade-off of performance and index size in existing SSD-based graph and cluster indexes: to improve throughput by 5.7, \times and 1.7, \times , these indexes have to pay a 5.8, \times storage amplification and 7.7, \times with respect to the dataset size, respectively. The root cause is that the coarse-grained access of SSD mismatches the fine-grained random read required by vector indexes with small amplification. This paper argues that second-tier memory, such as remote DRAM/NVM connected via RDMA or CXL, is a powerful storage for addressing the problem from a system’s perspective, thanks to its fine-grained access granularity. However, putting existing indexes – primarily designed for SSD – directly on second-tier memory cannot fully utilize its power. Meanwhile, second-tier memory still behaves more like storage, so using it as DRAM is also inefficient. To this end, we build a graph and cluster index that centers around the performance features of second-tier memory. With careful execution engine and index layout designs, we show that vector indexes can achieve optimal performance with orders of magnitude smaller index amplification, on a variety of second-tier memory devices. Based on our improved graph and vector indexes on second-tier memory, we further conduct a systematic study between them to facilitate developers choosing the right index for their workloads. Interestingly, the findings on the second-tier memory contradict the ones on SSDs.

IR-5-标题: TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation

链接: https://arxiv.org/abs/2405.03167
作者: Honghao Li, Yiwen Zhang, Yi Zhang, Lei Sang, Yun Yang
备注:

点击查看摘要

Abstract:Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR models resort to building complex network architectures to better capture intricate feature interactions or user behaviors. However, we identify two limitations in these models: (1) the samples given to the model are undifferentiated, which may lead the model to learn a larger number of easy samples in a single-minded manner while ignoring a smaller number of hard samples, thus reducing the model’s generalization ability; (2) differentiated feature interaction encoders are designed to capture different interactions information but receive consistent supervision signals, thereby limiting the effectiveness of the encoder. To bridge the identified gaps, this paper introduces a novel CTR prediction framework by integrating the plug-and-play Twin Focus (TF) Loss, Sample Selection Embedding Module (SSEM), and Dynamic Fusion Module (DFM), named the Twin Focus Framework for CTR (TF4CTR). Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample. Meanwhile, the TF Loss provides tailored supervision signals to both simple and complex encoders. Moreover, the DFM dynamically fuses the feature interaction information captured by the encoders, resulting in more accurate predictions. Experiments on five real-world datasets confirm the effectiveness and compatibility of the framework, demonstrating its capacity to enhance various representative baselines in a model-agnostic manner. To facilitate reproducible research, our open-sourced code and detailed running logs will be made available at: this https URL.

IR-6-标题: Vector Quantization for Recommender Systems: A Review and Outlook

链接: https://arxiv.org/abs/2405.03110
作者: Qijiong Liu, Xiaoyu Dong, Jiaren Xiao, Nuo Chen, Hengchang Hu, Jieming Zhu, Chenxu Zhu, Tetsuya Sakai, Xiao-Ming Wu
备注:

点击查看摘要

Abstract:Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large models and generative AI, vector quantization has gained popularity in recommender systems, establishing itself as a preferred solution. This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonomies of vector quantization methods for recommender systems (VQ4Rec), examining their applications from multiple perspectives. Further, it provides a thorough introduction to research efforts in diverse recommendation scenarios, including efficiency-oriented approaches and quality-oriented approaches. Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems. We hope this survey can pave the way for future researchers in the recommendation community and accelerate their exploration in this promising field.

IR-7-标题: Improve Temporal Awareness of LLM s for Sequential Recommendation

链接: https://arxiv.org/abs/2405.02778
作者: Zhendong Chu, Zichao Wang, Ruiyi Zhang, Yangfeng Ji, Hongning Wang, Tong Sun
备注: 10 pages

点击查看摘要

Abstract:Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data, such as sequential recommendation. In this paper, we aim to improve temporal awareness of LLMs by designing a principled prompting framework inspired by human cognitive processes. Specifically, we propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation. Besides, we emulate divergent thinking by aggregating LLM ranking results derived from these strategies. Evaluations on MovieLens-1M and Amazon Review datasets indicate that our proposed method significantly enhances the zero-shot capabilities of LLMs in sequential recommendation tasks.

IR-8-标题: Sign-Guided Bipartite Graph Hashing for Hamming Space Search

链接: https://arxiv.org/abs/2405.02716
作者: Xueyi Wu
备注:

点击查看摘要

Abstract:Bipartite graph hashing (BGH) is extensively used for Top-K search in Hamming space at low storage and inference costs. Recent research adopts graph convolutional hashing for BGH and has achieved the state-of-the-art performance. However, the contributions of its various influencing factors to hashing performance have not been explored in-depth, including the same/different sign count between two binary embeddings during Hamming space search (sign property), the contribution of sub-embeddings at each layer (model property), the contribution of different node types in the bipartite graph (node property), and the combination of augmentation methods. In this work, we build a lightweight graph convolutional hashing model named LightGCH by mainly removing the augmentation methods of the state-of-the-art model BGCH. By analyzing the contributions of each layer and node type to performance, as well as analyzing the Hamming similarity statistics at each layer, we find that the actual neighbors in the bipartite graph tend to have low Hamming similarity at the shallow layer, and all nodes tend to have high Hamming similarity at the deep layers in LightGCH. To tackle these problems, we propose a novel sign-guided framework SGBGH to make improvement, which uses sign-guided negative sampling to improve the Hamming similarity of neighbors, and uses sign-aware contrastive learning to help nodes learn more uniform representations. Experimental results show that SGBGH outperforms BGCH and LightGCH significantly in embedding quality.

IR-9-标题: RLStop: A Reinforcement Learning Stopping Method for TAR SIGIR2024

链接: https://arxiv.org/abs/2405.02525
作者: Reem Bin-Hezam, Mark Stevenson
备注: Accepted at SIGIR 2024

点击查看摘要

Abstract:We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications. RLStop is trained on example rankings using a reward function to identify the optimal point to stop examining documents. Experiments at a range of target recall levels on multiple benchmark datasets (CLEF e-Health, TREC Total Recall, and Reuters RCV1) demonstrated that RLStop substantially reduces the workload required to screen a document collection for relevance. RLStop outperforms a wide range of alternative approaches, achieving performance close to the maximum possible for the task under some circumstances.

IR-10-标题: Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models SIGIR2024

链接: https://arxiv.org/abs/2405.02503
作者: Catherine Chen, Jack Merullo, Carsten Eickhoff
备注: 10 pages, 10 figures, accepted at SIGIR 2024 as perspective paper

点击查看摘要

Abstract:Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still largely unknown. Existing approaches for analyzing neural ranker behavior with respect to IR properties rely either on assessing overall model behavior or employing probing methods that may offer an incomplete understanding of causal mechanisms. To provide a more granular understanding of internal model decision-making processes, we propose the use of causal interventions to reverse engineer neural rankers, and demonstrate how mechanistic interpretability methods can be used to isolate components satisfying term-frequency axioms within a ranking model. We identify a group of attention heads that detect duplicate tokens in earlier layers of the model, then communicate with downstream heads to compute overall document relevance. More generally, we propose that this style of mechanistic analysis opens up avenues for reverse engineering the processes neural retrieval models use to compute relevance. This work aims to initiate granular interpretability efforts that will not only benefit retrieval model development and training, but ultimately ensure safer deployment of these models.

人工智能

AI-0-标题: Prompting Task Trees using Gemini: Methodologies and Insights

链接: https://arxiv.org/abs/2405.03671
作者: Pallavi Tandra
备注:

点击查看摘要

Abstract:Robots are the future of every technology where every advanced technology eventually will be used to make robots which are more efficient. The major challenge today is to train the robots exactly and empathetically using knowledge representation. This paper gives you insights of how we can use unstructured knowledge representation and convert them to meaningful structured representation with the help of prompt engineering which can be eventually used in the robots to make help them understand how human brain can make wonders with the minimal data or objects can providing to them.

AI-1-标题: ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection

链接: https://arxiv.org/abs/2405.03666
作者: Arpit Bahety, Priyanka Mandikal, Ben Abbatematteo, Roberto Martín-Martín
备注: 16 pages

点击查看摘要

Abstract:Bimanual manipulation is a longstanding challenge in robotics due to the large number of degrees of freedom and the strict spatial and temporal synchronization required to generate meaningful behavior. Humans learn bimanual manipulation skills by watching other humans and by refining their abilities through play. In this work, we aim to enable robots to learn bimanual manipulation behaviors from human video demonstrations and fine-tune them through interaction. Inspired by seminal work in psychology and biomechanics, we propose modeling the interaction between two hands as a serial kinematic linkage – as a screw motion, in particular, that we use to define a new action space for bimanual manipulation: screw actions. We introduce ScrewMimic, a framework that leverages this novel action representation to facilitate learning from human demonstration and self-supervised policy fine-tuning. Our experiments demonstrate that ScrewMimic is able to learn several complex bimanual behaviors from a single human video demonstration, and that it outperforms baselines that interpret demonstrations and fine-tune directly in the original space of motion of both arms. For more information and video results, this https URL

AI-2-标题: Can LLM s Deeply Detect Complex Malicious Queries? A Framework for Jailbreaking via Obfuscating Intent

链接: https://arxiv.org/abs/2405.03654
作者: Shang Shang, Xinqiang Zhao, Zhongjiang Yao, Yepeng Yao, Liya Su, Zijing Fan, Xiaodan Zhang, Zhengwei Jiang
备注:

点击查看摘要

Abstract:To demonstrate and address the underlying maliciousness, we propose a theoretical hypothesis and analytical approach, and introduce a new black-box jailbreak attack methodology named IntentObfuscator, exploiting this identified flaw by obfuscating the true intentions behind user prompts.This approach compels LLMs to inadvertently generate restricted content, bypassing their built-in content security measures. We detail two implementations under this framework: “Obscure Intention” and “Create Ambiguity”, which manipulate query complexity and ambiguity to evade malicious intent detection effectively. We empirically validate the effectiveness of the IntentObfuscator method across several models, including ChatGPT-3.5, ChatGPT-4, Qwen and Baichuan, achieving an average jailbreak success rate of 69.21%. Notably, our tests on ChatGPT-3.5, which claims 100 million weekly active users, achieved a remarkable success rate of 83.65%. We also extend our validation to diverse types of sensitive content like graphic violence, racism, sexism, political sensitivity, cybersecurity threats, and criminal skills, further proving the substantial impact of our findings on enhancing ‘Red Team’ strategies against LLM content security frameworks.

AI-3-标题: When LLM s Meet Cybersecurity: A Systematic Literature Review

链接: https://arxiv.org/abs/2405.03644
作者: Jie Zhang, Haoyu Bu, Hui Wen, Yu Chen, Lun Li, Hongsong Zhu
备注: 36 pages, 7 figures

点击查看摘要

Abstract:The rapid advancements in large language models (LLMs) have opened new avenues across various fields, including cybersecurity, which faces an ever-evolving threat landscape and need for innovative technologies. Despite initial explorations into the application of LLMs in cybersecurity, there is a lack of a comprehensive overview of this research area. This paper bridge this gap by providing a systematic literature review, encompassing an analysis of over 180 works, spanning across 25 LLMs and more than 10 downstream scenarios. Our comprehensive overview addresses three critical research questions: the construction of cybersecurity-oriented LLMs, LLMs’ applications in various cybersecurity tasks, and the existing challenges and further research in this area. This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices, and serve as a valuable resource for applying LLMs in this doamin. We also maintain and regularly updated list of practical guides on LLMs for cybersecurity at this https URL.

AI-4-标题: Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

链接: https://arxiv.org/abs/2405.03620
作者: Meryam Chaieb, Mostafa Anouar Ghorab, Mohamed Aymen Saied
备注:

点击查看摘要

Abstract:As cyber threats and malware attacks increasingly alarm both individuals and businesses, the urgency for proactive malware countermeasures intensifies. This has driven a rising interest in automated machine learning solutions. Transformers, a cutting-edge category of attention-based deep learning methods, have demonstrated remarkable success. In this paper, we present BERTroid, an innovative malware detection model built on the BERT architecture. Overall, BERTroid emerged as a promising solution for combating Android malware. Its ability to outperform state-of-the-art solutions demonstrates its potential as a proactive defense mechanism against malicious software attacks. Additionally, we evaluate BERTroid on multiple datasets to assess its performance across diverse scenarios. In the dynamic landscape of cybersecurity, our approach has demonstrated promising resilience against the rapid evolution of malware on Android systems. While the machine learning model captures broad patterns, we emphasize the role of manual validation for deeper comprehension and insight into these behaviors. This human intervention is critical for discerning intricate and context-specific behaviors, thereby validating and reinforcing the model’s findings.

AI-5-标题: A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama

链接: https://arxiv.org/abs/2405.03616
作者: Vlad-Andrei Cursaru, Laura Duits, Joel Milligan, Damla Ural, Berta Rodriguez Sanchez, Vincenzo Stoico, Ivano Malavolta
备注:

点击查看摘要

Abstract:Context. Nowadays, 83% of software developers use Large Language Models (LLMs) to generate code. LLMs recently became essential to increase the productivity of software developers and decrease the time and cost of software development. Developers ranging from novices to experts use LLM tools not only to detect and patch bugs, but also to integrate generated code into their software. However, as of today there is no objective assessment of the energy efficiency of the source code generated by LLM tools. Released in August 2023, Code Llama is one of the most recent LLM tools. Goal. In this paper, we present an empirical study that assesses the energy efficiency of Code Llama with respect to human-written source code. Method. We design an experiment involving three human-written benchmarks implemented in C++, JavaScript, and Python. We ask Code Llama to generate the code of the benchmarks using different prompts and temperatures. Therefore, we execute both implementations and profile their energy efficiency. Results. Our study shows that the energy efficiency of code generated by Code Llama is heavily-dependent on the chosen programming language and the specific code problem at hand. Also, human implementations tend to be more energy efficient overall, with generated JavaScript code outperforming its human counterpart. Moreover, explicitly asking Code Llama to generate energy-efficient code results in an equal or worse energy efficiency, as well as using different temperatures seems not to affect the energy efficiency of generated code. Conclusions. According to our results, code generated using Code Llama does not guarantee energy efficiency, even when prompted to do so. Therefore, software developers should evaluate the energy efficiency of generated code before integrating it into the software system under development.

AI-6-标题: Deep Space Separable Distillation for Lightweight Acoustic Scene Classification

链接: https://arxiv.org/abs/2405.03567
作者: ShuQi Ye, Yuan Tian
备注:

点击查看摘要

Abstract:Acoustic scene classification (ASC) is highly important in the real world. Recently, deep learning-based methods have been widely employed for acoustic scene classification. However, these methods are currently not lightweight enough as well as their performance is not satisfactory. To solve these problems, we propose a deep space separable distillation network. Firstly, the network performs high-low frequency decomposition on the log-mel spectrogram, significantly reducing computational complexity while maintaining model performance. Secondly, we specially design three lightweight operators for ASC, including Separable Convolution (SC), Orthonormal Separable Convolution (OSC), and Separable Partial Convolution (SPC). These operators exhibit highly efficient feature extraction capabilities in acoustic scene classification tasks. The experimental results demonstrate that the proposed method achieves a performance gain of 9.8% compared to the currently popular deep learning methods, while also having smaller parameter count and computational complexity.

AI-7-标题: Exploring knowledge graph-based neural-symbolic system from application perspective

链接: https://arxiv.org/abs/2405.03524
作者: Shenzhe Zhu
备注:

点击查看摘要

Abstract:The rapid advancement in artificial intelligence (AI), particularly through deep neural networks, has catalyzed significant progress in fields such as vision and text processing. Nonetheless, the pursuit of AI systems that exhibit human-like reasoning and interpretability continues to pose a substantial challenge. The Neural-Symbolic paradigm, which integrates the deep learning prowess of neural networks with the reasoning capabilities of symbolic systems, presents a promising pathway toward developing more transparent and comprehensible AI systems. Within this paradigm, the Knowledge Graph (KG) emerges as a crucial element, offering a structured and dynamic method for representing knowledge through interconnected entities and relationships, predominantly utilizing the triple (subject, predicate, object). This paper explores recent advancements in neural-symbolic integration based on KG, elucidating how KG underpins this integration across three key categories: enhancing the reasoning and interpretability of neural networks through the incorporation of symbolic knowledge (Symbol for Neural), refining the completeness and accuracy of symbolic systems via neural network methodologies (Neural for Symbol), and facilitating their combined application in Hybrid Neural-Symbolic Integration. It highlights current trends and proposes directions for future research in the domain of Neural-Symbolic AI.

AI-8-标题: Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

链接: https://arxiv.org/abs/2405.03406
作者: Malte Luttermann, Edgar Baake, Juljan Bouchagiar, Benjamin Gebel, Philipp Grüning, Dilini Manikwadura, Franziska Schollemann, Elisa Teifke, Philipp Rostalski, Ralf Möller
备注: Accepted to the German Journal of Artificial Intelligence

点击查看摘要

Abstract:Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients.

AI-9-标题: Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G

链接: https://arxiv.org/abs/2405.03372
作者: Xiaoxue Yu, Xingfu Yi, Rongpeng Li, Fei Wang, Chenghui Peng, Zhifeng Zhao, Honggang Zhang
备注: 7 pages, 6 figures

点击查看摘要

Abstract:In the evolution towards 6G, integrating Artificial Intelligence (AI) with advanced network infrastructure emerges as a pivotal strategy for enhancing network intelligence and resource utilization. Existing distributed learning frameworks like Federated Learning and Split Learning often struggle with significant challenges in dynamic network environments including high synchronization demands, costly communication overheads, severe computing resource consumption, and data heterogeneity across network nodes. These obstacles hinder the applications of ubiquitous computing capabilities of 6G networks, especially in light of the trend of escalating model parameters and training data volumes. To address these challenges effectively, this paper introduces “Snake Learning”, a cost-effective distributed learning framework. Specifically, Snake Learning respects the heterogeneity of inter-node computing capability and local data distribution in 6G networks, and sequentially trains the designated part of model layers on individual nodes. This layer-by-layer serpentine update mechanism contributes to significantly reducing the requirements for storage, memory and communication during the model training phase, and demonstrates superior adaptability and efficiency for both Computer Vision (CV) training and Large Language Model (LLM) fine-tuning tasks across homogeneous and heterogeneous data distributions.

AI-10-标题: Functional Equivalence with NARS

链接: https://arxiv.org/abs/2405.03340
作者: Robert Johansson, Patrick Hammer, Tony Lofthouse
备注:

点击查看摘要

Abstract:This study explores the concept of functional equivalence within the framework of the Non-Axiomatic Reasoning System (NARS), specifically through OpenNARS for Applications (ONA). Functional equivalence allows organisms to categorize and respond to varied stimuli based on their utility rather than perceptual similarity, thus enhancing cognitive efficiency and adaptability. In this study, ONA was modified to allow the derivation of functional equivalence. This paper provides practical examples of the capability of ONA to apply learned knowledge across different functional situations, demonstrating its utility in complex problem-solving and decision-making. An extended example is included, where training of ONA aimed to learn basic human-like language abilities, using a systematic procedure in relating spoken words, objects and written words. The research carried out as part of this study extends the understanding of functional equivalence in AGI systems, and argues for its necessity for level of flexibility in learning and adapting necessary for human-level AGI.

AI-11-标题: Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review

链接: https://arxiv.org/abs/2405.03305
作者: Harry Robertshaw, Lennart Karstensen, Benjamin Jackson, Hadi Sadati, Kawal Rhode, Sebastien Ourselin, Alejandro Granados, Thomas C Booth
备注: Abstract shortened for arXiv character limit

点击查看摘要

Abstract:Purpose: Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. This systematic review explores recent literature to assess the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous endovascular intervention navigation. Methods: PubMed and IEEEXplore databases were queried. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following PRISMA, articles were assessed using QUADAS-2. PROSPERO: CRD42023392259. Results: Among 462 studies, fourteen met inclusion criteria. Reinforcement learning (9/14, 64%) and learning from demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. Studies predominantly utilised physical phantoms (10/14, 71%) and in silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while simple non-anatomical vessel platforms were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalisability were present across studies. No procedures were performed on patients in any of the studies reviewed. Studies lacked patient selection criteria, reference standards, and reproducibility, resulting in low clinical evidence levels. Conclusions: AI’s potential in autonomous endovascular navigation is promising, but in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come.

AI-12-标题: Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization

链接: https://arxiv.org/abs/2405.03151
作者: Xinye Sha
备注:

点击查看摘要

Abstract:In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. After optimization and adjustment, the mean absolute error (MAE) of the model gradually decreases from 0.11 to 0.01 and tends to be stable, indicating that the model prediction effect is gradually close to the real value. The results on the test set show that the time series algorithm optimized based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) is able to accurately predict the stock prices, and is highly consistent with the actual price trends and values, with strong generalization ability. The MAE on the test set is 2.41, the MSE is 9.84, the RMSE is 3.13, and the R2 is 0.87. This research result not only provides a novel stock price prediction method, but also provides a useful reference for financial market analysis using computer technology and big data.

AI-13-标题: Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning

链接: https://arxiv.org/abs/2405.03113
作者: Caleb Chuck, Carl Qi, Michael J. Munje, Shuozhe Li, Max Rudolph, Chang Shi, Siddhant Agarwal, Harshit Sikchi, Abhinav Peri, Sarthak Dayal, Evan Kuo, Kavan Mehta, Anthony Wang, Peter Stone, Amy Zhang, Scott Niekum
备注:

点击查看摘要

Abstract:Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail. To effectively reflect this challenging category of tasks, we introduce a dynamic, interactive RL testbed based on robot air hockey. By augmenting air hockey with a large family of tasks ranging from easy tasks like reaching, to challenging ones like pushing a block by hitting it with a puck, as well as goal-based and human-interactive tasks, our testbed allows a varied assessment of RL capabilities. The robot air hockey testbed also supports sim-to-real transfer with three domains: two simulators of increasing fidelity and a real robot system. Using a dataset of demonstration data gathered through two teleoperation systems: a virtualized control environment, and human shadowing, we assess the testbed with behavior cloning, offline RL, and RL from scratch.

AI-14-标题: High Order Reasoning for Time Critical Recommendation in Evidence-based Medicine

链接: https://arxiv.org/abs/2405.03010
作者: Manjiang Yu, Xue Li
备注: 13 pages, 15 figures

点击查看摘要

Abstract:In time-critical decisions, human decision-makers can interact with AI-enabled situation-aware software to evaluate many imminent and possible scenarios, retrieve billions of facts, and estimate different outcomes based on trillions of parameters in a fraction of a second. In high-order reasoning, “what-if” questions can be used to challenge the assumptions or pre-conditions of the reasoning, “why-not” questions can be used to challenge on the method applied in the reasoning, “so-what” questions can be used to challenge the purpose of the decision, and “how-about” questions can be used to challenge the applicability of the method. When above high-order reasoning questions are applied to assist human decision-making, it can help humans to make time-critical decisions and avoid false-negative or false-positive types of errors. In this paper, we present a model of high-order reasoning to offer recommendations in evidence-based medicine in a time-critical fashion for the applications in ICU. The Large Language Model (LLM) is used in our system. The experiments demonstrated the LLM exhibited optimal performance in the “What-if” scenario, achieving a similarity of 88.52% with the treatment plans of human doctors. In the “Why-not” scenario, the best-performing model tended to opt for alternative treatment plans in 70% of cases for patients who died after being discharged from the ICU. In the “So-what” scenario, the optimal model provided a detailed analysis of the motivation and significance of treatment plans for ICU patients, with its reasoning achieving a similarity of 55.6% with actual diagnostic information. In the “How-about” scenario, the top-performing LLM demonstrated a content similarity of 66.5% in designing treatment plans transferring for similar diseases. Meanwhile, LLMs managed to predict the life status of patients after their discharge from the ICU with an accuracy of 70%.

AI-15-标题: Explainable Malware Detection with Tailored Logic Explained Networks

链接: https://arxiv.org/abs/2405.03009
作者: Peter Anthony, Francesco Giannini, Michelangelo Diligenti, Martin Homola, Marco Gori, Stefan Balogh, Jan Mojzis
备注:

点击查看摘要

Abstract:Malware detection is a constant challenge in cybersecurity due to the rapid development of new attack techniques. Traditional signature-based approaches struggle to keep pace with the sheer volume of malware samples. Machine learning offers a promising solution, but faces issues of generalization to unseen samples and a lack of explanation for the instances identified as malware. However, human-understandable explanations are especially important in security-critical fields, where understanding model decisions is crucial for trust and legal compliance. While deep learning models excel at malware detection, their black-box nature hinders explainability. Conversely, interpretable models often fall short in performance. To bridge this gap in this application domain, we propose the use of Logic Explained Networks (LENs), which are a recently proposed class of interpretable neural networks providing explanations in the form of First-Order Logic (FOL) rules. This paper extends the application of LENs to the complex domain of malware detection, specifically using the large-scale EMBER dataset. In the experimental results we show that LENs achieve robustness that exceeds traditional interpretable methods and that are rivaling black-box models. Moreover, we introduce a tailored version of LENs that is shown to generate logic explanations with higher fidelity with respect to the model’s predictions.

AI-16-标题: RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification

链接: https://arxiv.org/abs/2405.02996
作者: June-Woo Kim, Miika Toikkanen, Sangmin Bae, Minseok Kim, Ho-Young Jung
备注: Accepted EMBC 2024

点击查看摘要

Abstract:Recent advancements in AI have democratized its deployment as a healthcare assistant. While pretrained models from large-scale visual and audio datasets have demonstrably generalized to this task, surprisingly, no studies have explored pretrained speech models, which, as human-originated sounds, intuitively would share closer resemblance to lung sounds. This paper explores the efficacy of pretrained speech models for respiratory sound classification. We find that there is a characterization gap between speech and lung sound samples, and to bridge this gap, data augmentation is essential. However, the most widely used augmentation technique for audio and speech, SpecAugment, requires 2-dimensional spectrogram format and cannot be applied to models pretrained on speech waveforms. To address this, we propose RepAugment, an input-agnostic representation-level augmentation technique that outperforms SpecAugment, but is also suitable for respiratory sound classification with waveform pretrained models. Experimental results show that our approach outperforms the SpecAugment, demonstrating a substantial improvement in the accuracy of minority disease classes, reaching up to 7.14%.

AI-17-标题: Multi- Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks

链接: https://arxiv.org/abs/2405.02972
作者: Siyuan Li, Xi Lin, Hansong Xu, Kun Hua, Xiaomin Jin, Gaolei Li, Jianhua Li
备注:

点击查看摘要

Abstract:Currently, the generative model has garnered considerable attention due to its application in addressing the challenge of scarcity of abnormal samples in the industrial Internet of Things (IoT). However, challenges persist regarding the edge deployment of generative models and the optimization of joint edge AI-generated content (AIGC) tasks. In this paper, we focus on the edge optimization of AIGC task execution and propose GMEL, a generative model-driven industrial AIGC collaborative edge learning framework. This framework aims to facilitate efficient few-shot learning by leveraging realistic sample synthesis and edge-based optimization capabilities. First, a multi-task AIGC computational offloading model is presented to ensure the efficient execution of heterogeneous AIGC tasks on edge servers. Then, we propose an attention-enhanced multi-agent reinforcement learning (AMARL) algorithm aimed at refining offloading policies within the IoT system, thereby supporting generative model-driven edge learning. Finally, our experimental results demonstrate the effectiveness of the proposed algorithm in optimizing the total system latency of the edge-based AIGC task completion.

AI-18-标题: Robust Collaborative Perception without External Localization and Clock Devices ICRA2024

链接: https://arxiv.org/abs/2405.02965
作者: Zixing Lei, Zhenyang Ni, Ruize Han, Shuo Tang, Chen Feng, Siheng Chen, Yanfeng Wang
备注: 6pages, accepted to ICRA 2024

点击查看摘要

Abstract:A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception, which seeks to improve perception abilities through information exchange among agents. To achieve this spatial-temporal alignment, traditional methods depend on external devices to provide localization and clock signals. However, hardware-generated signals could be vulnerable to noise and potentially malicious attack, jeopardizing the precision of spatial-temporal alignment. Rather than relying on external hardwares, this work proposes a novel approach: aligning by recognizing the inherent geometric patterns within the perceptual data of various agents. Following this spirit, we propose a robust collaborative perception system that operates independently of external localization and clock devices. The key module of our system,~\emphFreeAlign, constructs a salient object graph for each agent based on its detected boxes and uses a graph neural network to identify common subgraphs between agents, leading to accurate relative pose and time. We validate \emphFreeAlign on both real-world and simulated datasets. The results show that, the ~\emphFreeAlign empowered robust collaborative perception system perform comparably to systems relying on precise localization and clock devices.

AI-19-标题: Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agent s

链接: https://arxiv.org/abs/2405.02957
作者: Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, Yunghwei Lai, Xinhui Kang, Weizhi Ma, Yang Liu
备注:

点击查看摘要

Abstract:In this paper, we introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness. All patients, nurses, and doctors are autonomous agents powered by large language models (LLMs). Our central goal is to enable a doctor agent to learn how to treat illness within the simulacrum. To do so, we propose a method called MedAgent-Zero. As the simulacrum can simulate disease onset and progression based on knowledge bases and LLMs, doctor agents can keep accumulating experience from both successful and unsuccessful cases. Simulation experiments show that the treatment performance of doctor agents consistently improves on various tasks. More interestingly, the knowledge the doctor agents have acquired in Agent Hospital is applicable to real-world medicare benchmarks. After treating around ten thousand patients (real-world doctors may take over two years), the evolved doctor agent achieves a state-of-the-art accuracy of 93.06% on a subset of the MedQA dataset that covers major respiratory diseases. This work paves the way for advancing the applications of LLM-powered agent techniques in medical scenarios.

AI-20-标题: Halfway Escape Optimization: A Quantum-Inspired Solution for Complex Optimization Problems

链接: https://arxiv.org/abs/2405.02850
作者: Jiawen Li, Anwar PP Abdul Majeed, Pascal Lefevre
备注:

点击查看摘要

Abstract:This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a novel quantum-inspired metaheuristic designed to address complex optimization problems characterized by rugged landscapes and high-dimensionality with an efficient convergence rate. The study presents a comprehensive comparative evaluation of HEO’s performance against established optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Grey Wolf Optimizer (GWO), and Quantum behaved Particle Swarm Optimization (QPSO). The primary analysis encompasses 14 benchmark functions with dimension 30, demonstrating HEO’s effectiveness and adaptability in navigating complex optimization landscapes and providing valuable insights into its performance. The simple test of HEO in Traveling Salesman Problem (TSP) also infers its feasibility in real-time applications.

AI-21-标题: Responsible AI: Portraits with Intelligent Bibliometrics

链接: https://arxiv.org/abs/2405.02846
作者: Yi Zhang, Mengjia Wu, Guangquan Zhang, Jie Lu
备注: 14 pages, 9 figures

点击查看摘要

Abstract:Shifting the focus from principles to practical implementation, responsible artificial intelligence (AI) has garnered considerable attention across academia, industry, and society at large. Despite being in its nascent stages, this emerging field grapples with nebulous concepts and intricate knowledge frameworks. By analyzing three prevailing concepts - explainable AI, trustworthy AI, and ethical AI, this study defined responsible AI and identified its core principles. Methodologically, this study successfully demonstrated the implementation of leveraging AI’s capabilities into bibliometrics for enhanced knowledge discovery and the cross-validation of experimentally examined models with domain insights. Empirically, this study investigated 17,799 research articles contributed by the AI community since 2015. This involves recognizing key technological players and their relationships, unveiling the topical landscape and hierarchy of responsible AI, charting its evolution, and elucidating the interplay between the responsibility principles and primary AI techniques. An analysis of a core cohort comprising 380 articles from multiple disciplines captures the most recent advancements in responsible AI. As one of the pioneering bibliometric studies dedicated to exploring responsible AI, this study will provide comprehensive macro-level insights, enhancing the understanding of responsible AI while furnishing valuable knowledge support for AI regulation and governance initiatives.

AI-22-标题: Mozarts Touch: A Lightweight Multi-modal Music Generation Framework Based on Pre-Trained Large Models ACM-MM2024

链接: https://arxiv.org/abs/2405.02801
作者: Tianze Xu, Jiajun Li, Xuesong Chen, Yinrui Yao, Shuchang Liu
备注: 7 pages, 2 figures, submitted to ACM MM 2024

点击查看摘要

Abstract:In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the generation of music, images, and other forms of artistic expression across various industries. However, researches on general multi-modal music generation model remain scarce. To fill this gap, we propose a multi-modal music generation framework Mozart’s Touch. It could generate aligned music with the cross-modality inputs, such as images, videos and text. Mozart’s Touch is composed of three main components: Multi-modal Captioning Module, Large Language Model (LLM) Understanding & Bridging Module, and Music Generation Module. Unlike traditional approaches, Mozart’s Touch requires no training or fine-tuning pre-trained models, offering efficiency and transparency through clear, interpretable prompts. We also introduce “LLM-Bridge” method to resolve the heterogeneous representation problems between descriptive texts of different modalities. We conduct a series of objective and subjective evaluations on the proposed model, and results indicate that our model surpasses the performance of current state-of-the-art models. Our codes and examples is availble at: this https URL

AI-23-标题: The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses

链接: https://arxiv.org/abs/2405.02711
作者: Jordyn Young, Laala M Jawara, Diep N Nguyen, Brian Daly, Jina Huh-Yoo, Afsaneh Razi
备注:

点击查看摘要

Abstract:Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people’s main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human- and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people’s well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised.

AI-24-标题: Quranic Audio Dataset : Crowdsourced and Labeled Recitation from Non-Arabic Speakers

链接: https://arxiv.org/abs/2405.02675
作者: Raghad Salameh, Mohamad Al Mdfaa, Nursultan Askarbekuly, Manuel Mazzara
备注:

点击查看摘要

Abstract:This paper addresses the challenge of learning to recite the Quran for non-Arabic speakers. We explore the possibility of crowdsourcing a carefully annotated Quranic dataset, on top of which AI models can be built to simplify the learning process. In particular, we use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets. We integrated the API into an existing mobile application called NamazApp to collect audio recitations. We developed a crowdsourcing platform called Quran Voice for annotating the gathered audio assets. As a result, we have collected around 7000 Quranic recitations from a pool of 1287 participants across more than 11 non-Arabic countries, and we have annotated 1166 recitations from the dataset in six categories. We have achieved a crowd accuracy of 0.77, an inter-rater agreement of 0.63 between the annotators, and 0.89 between the labels assigned by the algorithm and the expert judgments.

AI-25-标题: Med Prompt Extract (Medical Data Extraction Tool): Anonymization and Hi-fidelity Automated data extraction using NLP and prompt engineering

链接: https://arxiv.org/abs/2405.02664
作者: Roomani Srivastava, Suraj Prasad, Lipika Bhat, Sarvesh Deshpande, Barnali Das, Kshitij Jadhav
备注:

点击查看摘要

Abstract:A major roadblock in the seamless digitization of medical records remains the lack of interoperability of existing records. Extracting relevant medical information required for further treatment planning or even research is a time consuming labour intensive task involving the much valuable time of doctors. In this demo paper we present, MedPromptExtract an automated tool using a combination of semi supervised learning, large language models, natural lanuguage processing and prompt engineering to convert unstructured medical records to structured data which is amenable to further analysis.

AI-26-标题: Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi- Agent Reinforcement Learning IJCAI2024

链接: https://arxiv.org/abs/2405.02654
作者: Tianyu Ren, Xiao-Jun Zeng
备注: Accepted at IJCAI 2024 (33nd International Joint Conference on Artificial Intelligence - Jeju)

点击查看摘要

Abstract:The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner’s Dilemma game. This framework allows agents to select dilemma strategies and interacting neighbours based on their long-term experiences, differing from existing research that relies on preset social norms or external incentives. By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction. The results indicate that long-term experience enables agents to develop the ability to identify non-cooperative neighbours and exhibit a preference for interaction with cooperative ones. This emergent self-organizing behaviour leads to the clustering of agents with similar strategies, thereby increasing network reciprocity and enhancing group cooperation.

AI-27-标题: Isopignistic Canonical Decomposition via Belief Evolution Network

链接: https://arxiv.org/abs/2405.02653
作者: Qianli Zhou, Tianxiang Zhan, Yong Deng
备注:

点击查看摘要

Abstract:Developing a general information processing model in uncertain environments is fundamental for the advancement of explainable artificial intelligence. Dempster-Shafer theory of evidence is a well-known and effective reasoning method for representing epistemic uncertainty, which is closely related to subjective probability theory and possibility theory. Although they can be transformed to each other under some particular belief structures, there remains a lack of a clear and interpretable transformation process, as well as a unified approach for information processing. In this paper, we aim to address these issues from the perspectives of isopignistic belief functions and the hyper-cautious transferable belief model. Firstly, we propose an isopignistic transformation based on the belief evolution network. This transformation allows for the adjustment of the information granule while retaining the potential decision outcome. The isopignistic transformation is integrated with a hyper-cautious transferable belief model to establish a new canonical decomposition. This decomposition offers a reverse path between the possibility distribution and its isopignistic mass functions. The result of the canonical decomposition, called isopignistic function, is an identical information content distribution to reflect the propensity and relative commitment degree of the BPA. Furthermore, this paper introduces a method to reconstruct the basic belief assignment by adjusting the isopignistic function. It explores the advantages of this approach in modeling and handling uncertainty within the hyper-cautious transferable belief model. More general, this paper establishes a theoretical basis for building general models of artificial intelligence based on probability theory, Dempster-Shafer theory, and possibility theory.

AI-28-标题: MEXGEN: An Effective and Efficient Information Gain Approximation for Information Gathering Path Planning

链接: https://arxiv.org/abs/2405.02605
作者: Joshua Chesser, Thuraiappah Sathyan, Damith C. Ranasinghe
备注: Accepted to IEEE Robotics and Automation Letters (RA-L)(Demo Video: this https URL)

点击查看摘要

Abstract:Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realizing autonomy demands online planning algorithms to solve sequential decision making problems under uncertainty; because, objects of interest are often dynamic, object state, such as location is not directly observable and are obtained from noisy measurements. Such planning problems are notoriously difficult due to the combinatorial nature of predicting the future to make optimal decisions. For information theoretic planning algorithms, we develop a computationally efficient and effective approximation for the difficult problem of predicting the likely sensor measurements from uncertain belief states. The approach more accurately predicts information gain from information gathering actions. Our theoretical analysis proves the proposed formulation achieves a lower prediction error than the current efficient-method. We demonstrate improved performance gains in radio-source tracking and localization problems using extensive simulated and field experiments with a multirotor aerial robot.

AI-29-标题: Explainable Interface for Human-Autonomy Teaming: A Survey

链接: https://arxiv.org/abs/2405.02583
作者: Xiangqi Kong, Yang Xing, Antonios Tsourdos, Ziyue Wang, Weisi Guo, Adolfo Perrusquia, Andreas Wikander
备注: 45 pages, 9 figures

点击查看摘要

Abstract:Nowadays, large-scale foundation models are being increasingly integrated into numerous safety-critical applications, including human-autonomy teaming (HAT) within transportation, medical, and defence domains. Consequently, the inherent ‘black-box’ nature of these sophisticated deep neural networks heightens the significance of fostering mutual understanding and trust between humans and autonomous systems. To tackle the transparency challenges in HAT, this paper conducts a thoughtful study on the underexplored domain of Explainable Interface (EI) in HAT systems from a human-centric perspective, thereby enriching the existing body of research in Explainable Artificial Intelligence (XAI). We explore the design, development, and evaluation of EI within XAI-enhanced HAT systems. To do so, we first clarify the distinctions between these concepts: EI, explanations and model explainability, aiming to provide researchers and practitioners with a structured understanding. Second, we contribute to a novel framework for EI, addressing the unique challenges in HAT. Last, our summarized evaluation framework for ongoing EI offers a holistic perspective, encompassing model performance, human-centered factors, and group task objectives. Based on extensive surveys across XAI, HAT, psychology, and Human-Computer Interaction (HCI), this review offers multiple novel insights into incorporating XAI into HAT systems and outlines future directions.

AI-30-标题: Property GPT : LLM -driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation

链接: https://arxiv.org/abs/2405.02580
作者: Ye Liu, Yue Xue, Daoyuan Wu, Yuqiang Sun, Yi Li, Miaolei Shi, Yang Liu
备注:

点击查看摘要

Abstract:With recent advances in large language models (LLMs), this paper explores the potential of leveraging state-of-the-art LLMs, such as GPT-4, to transfer existing human-written properties (e.g., those from Certora auditing reports) and automatically generate customized properties for unknown code. To this end, we embed existing properties into a vector database and retrieve a reference property for LLM-based in-context learning to generate a new prop- erty for a given code. While this basic process is relatively straight- forward, ensuring that the generated properties are (i) compilable, (ii) appropriate, and (iii) runtime-verifiable presents challenges. To address (i), we use the compilation and static analysis feedback as an external oracle to guide LLMs in iteratively revising the generated properties. For (ii), we consider multiple dimensions of similarity to rank the properties and employ a weighted algorithm to identify the top-K properties as the final result. For (iii), we design a dedicated prover to formally verify the correctness of the generated prop- erties. We have implemented these strategies into a novel system called PropertyGPT, with 623 human-written properties collected from 23 Certora projects. Our experiments show that PropertyGPT can generate comprehensive and high-quality properties, achieving an 80% recall compared to the ground truth. It successfully detected 26 CVEs/attack incidents out of 37 tested and also uncovered 12 zero-day vulnerabilities, resulting in 8,256 bug bounty rewards.

AI-31-标题: New contexts old heuristics: How young people in India and the US trust online content in the age of generative AI

链接: https://arxiv.org/abs/2405.02522
作者: Rachel Xu, Nhu Le, Rebekah Park, Laura Murray, Vishnupriya Das, Devika Kumar, Beth Goldberg
备注: 14 pages

点击查看摘要

Abstract:We conducted an in-person ethnography in India and the US to investigate how young people (18-24) trusted online content, with a focus on generative AI (GenAI). We had four key findings about how young people use GenAI and determine what to trust online. First, when online, we found participants fluidly shifted between mindsets and emotional states, which we term “information modes.” Second, these information modes shaped how and why participants trust GenAI and how they applied literacy skills. In the modes where they spent most of their time, they eschewed literacy skills. Third, with the advent of GenAI, participants imported existing trust heuristics from familiar online contexts into their interactions with GenAI. Fourth, although study participants had reservations about GenAI, they saw it as a requisite tool to adopt to keep up with the times. Participants valued efficiency above all else, and used GenAI to further their goals quickly at the expense of accuracy. Our findings suggest that young people spend the majority of their time online not concerned with truth because they are seeking only to pass the time. As a result, literacy interventions should be designed to intervene at the right time, to match users’ distinct information modes, and to work with their existing fact-checking practices.

AI-32-标题: Knowledge Graph Extension by Entity Type Recognition

链接: https://arxiv.org/abs/2405.02463
作者: Daqian Shi
备注: PhD thesis

点击查看摘要

Abstract:Knowledge graphs have emerged as a sophisticated advancement and refinement of semantic networks, and their deployment is one of the critical methodologies in contemporary artificial intelligence. The construction of knowledge graphs is a multifaceted process involving various techniques, where researchers aim to extract the knowledge from existing resources for the construction since building from scratch entails significant labor and time costs. However, due to the pervasive issue of heterogeneity, the description diversity across different knowledge graphs can lead to mismatches between concepts, thereby impacting the efficacy of knowledge extraction. This Ph.D. study focuses on automatic knowledge graph extension, i.e., properly extending the reference knowledge graph by extracting and integrating concepts from one or more candidate knowledge graphs. We propose a novel knowledge graph extension framework based on entity type recognition. The framework aims to achieve high-quality knowledge extraction by aligning the schemas and entities across different knowledge graphs, thereby enhancing the performance of the extension. This paper elucidates three major contributions: (i) we propose an entity type recognition method exploiting machine learning and property-based similarities to enhance knowledge extraction; (ii) we introduce a set of assessment metrics to validate the quality of the extended knowledge graphs; (iii) we develop a platform for knowledge graph acquisition, management, and extension to benefit knowledge engineers practically. Our evaluation comprehensively demonstrated the feasibility and effectiveness of the proposed extension framework and its functionalities through quantitative experiments and case studies.

AI-33-标题: Controlled Query Evaluation through Epistemic Dependencies

链接: https://arxiv.org/abs/2405.02458
作者: Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
备注:

点击查看摘要

Abstract:In this paper, we propose the use of epistemic dependencies to express data protection policies in Controlled Query Evaluation (CQE), which is a form of confidentiality-preserving query answering over ontologies and databases. The resulting policy language goes significantly beyond those proposed in the literature on CQE so far, allowing for very rich and practically interesting forms of data protection rules. We show the expressive abilities of our framework and study the data complexity of CQE for (unions of) conjunctive queries when ontologies are specified in the Description Logic DL-Lite_R. Interestingly, while we show that the problem is in general intractable, we prove tractability for the case of acyclic epistemic dependencies by providing a suitable query rewriting algorithm. The latter result paves the way towards the implementation and practical application of this new approach to CQE.

AI-34-标题: Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning

链接: https://arxiv.org/abs/2405.02425
作者: Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin Riedmiller, Nicolas Heess
备注:

点击查看摘要

Abstract:We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perception, agile full-body control, and long-horizon planning in a dynamic, partially-observable, multi-agent domain. We rely on large-scale, simulation-based data generation to obtain complex behaviors from egocentric vision which can be successfully transferred to physical robots using low-cost sensors. To achieve adequate visual realism, our simulation combines rigid-body physics with learned, realistic rendering via multiple Neural Radiance Fields (NeRFs). We combine teacher-based multi-agent RL and cross-experiment data reuse to enable the discovery of sophisticated soccer strategies. We analyze active-perception behaviors including object tracking and ball seeking that emerge when simply optimizing perception-agnostic soccer play. The agents display equivalent levels of performance and agility as policies with access to privileged, ground-truth state. To our knowledge, this paper constitutes a first demonstration of end-to-end training for multi-agent robot soccer, mapping raw pixel observations to joint-level actions, that can be deployed in the real world. Videos of the game-play and analyses can be seen on our website this https URL .

AI-35-标题: Neuromorphic Correlates of Artificial Consciousness

链接: https://arxiv.org/abs/2405.02370
作者: Anwaar Ulhaq
备注: 13 Pages, 8 Figures

点击查看摘要

Abstract:The concept of neural correlates of consciousness (NCC), which suggests that specific neural activities are linked to conscious experiences, has gained widespread acceptance. This acceptance is based on a wealth of evidence from experimental studies, brain imaging techniques such as fMRI and EEG, and theoretical frameworks like integrated information theory (IIT) within neuroscience and the philosophy of mind. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations. It proposes the Neuromorphic Correlates of Artificial Consciousness (NCAC) as a theoretical framework. While the debate on artificial consciousness remains contentious due to our incomplete grasp of consciousness, this work may raise eyebrows and invite criticism. Nevertheless, this optimistic and forward-thinking approach is fueled by insights from the Human Brain Project, advancements in brain imaging like EEG and fMRI, and recent strides in AI and computing, including quantum and neuromorphic designs. Additionally, this paper outlines how machine learning can play a role in crafting artificial consciousness, aiming to realise machine consciousness and awareness in the future.

AI-36-标题: CodeGRAG: Extracting Composed Syntax Graphs for Retrieval Augmented Cross-Lingual Code Generation

链接: https://arxiv.org/abs/2405.02355
作者: Kounianhua Du, Renting Rui, Huacan Chai, Lingyue Fu, Wei Xia, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
备注:

点击查看摘要

Abstract:Utilizing large language models to generate codes has shown promising meaning in software development revolution. Despite the intelligence shown by the general large language models, their specificity in code generation can still be improved due to the syntactic gap and mismatched vocabulary existing among natural language and different programming languages. In addition, programming languages are inherently logical and complex, making them hard to be correctly generated. Existing methods rely on multiple prompts to the large language model to explore better solutions, which is expensive. In this paper, we propose Syntax Graph Retrieval Augmented Code Generation (CodeGRAG) to enhance the performance of LLMs in single-round code generation tasks. CodeGRAG extracts and summarizes the control flow and data flow of code blocks to fill the gap between programming languages and natural language. The extracted external structural knowledge models the inherent flows of code blocks, which can facilitate LLMs for better understanding of code syntax and serve as a bridge among different programming languages. CodeGRAG significantly improves the code generation ability of LLMs and can even offer performance gain for cross-lingual code generation, e.g., C++ for Python.

AI-37-标题: Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions

链接: https://arxiv.org/abs/2405.02345
作者: Kevin Ma, Daniele Grandi, Christopher McComb, Kosa Goucher-Lambert
备注: preprint of journal paper

点击查看摘要

Abstract:Access to large amounts of diverse design solutions can support designers during the early stage of the design process. In this paper, we explore the efficacy of large language models (LLM) in producing diverse design solutions, investigating the level of impact that parameter tuning and various prompt engineering techniques can have on the diversity of LLM-generated design solutions. Specifically, LLMs are used to generate a total of 4,000 design solutions across five distinct design topics, eight combinations of parameters, and eight different types of prompt engineering techniques, comparing each combination of parameter and prompt engineering method across four different diversity metrics. LLM-generated solutions are compared against 100 human-crowdsourced solutions in each design topic using the same set of diversity metrics. Results indicate that human-generated solutions consistently have greater diversity scores across all design topics. Using a post hoc logistic regression analysis we investigate whether these differences primarily exist at the semantic level. Results show that there is a divide in some design topics between humans and LLM-generated solutions, while others have no clear divide. Taken together, these results contribute to the understanding of LLMs’ capabilities in generating a large volume of diverse design solutions and offer insights for future research that leverages LLMs to generate diverse design solutions for a broad range of design tasks (e.g., inspirational stimuli).

AI-38-标题: The Birkhoff completion of finite lattices

链接: https://arxiv.org/abs/2405.02342
作者: Mohammad Abdulla, Johannes Hirth, Gerd Stumme
备注:

点击查看摘要

Abstract:We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille’s simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.

AI-39-标题: CausalDisco: Causal discovery using knowledge graph link prediction

链接: https://arxiv.org/abs/2405.02327
作者: Utkarshani Jaimini, Cory Henson, Amit P. Sheth
备注: 9 pages, 8 figures

点击查看摘要

Abstract:Causal discovery is a process of discovering new causal relations from observational data. Traditional causal discovery methods often suffer from issues related to missing data To address these issues, this paper presents a novel approach called CausalDisco that formulates causal discovery as a knowledge graph completion problem. More specifically, the task of discovering causal relations is mapped to the task of knowledge graph link prediction. CausalDisco supports two types of discovery: causal explanation and causal prediction. The causal relations have weights representing the strength of the causal association between entities in the knowledge graph. An evaluation of this approach uses a benchmark dataset of simulated videos for causal reasoning, CLEVRER-Humans, and compares the performance of multiple knowledge graph embedding algorithms. In addition, two distinct dataset splitting approaches are utilized within the evaluation: (1) random-based split, which is the method typically used to evaluate link prediction algorithms, and (2) Markov-based split, a novel data split technique for evaluating link prediction that utilizes the Markovian property of the causal relation. Results show that using weighted causal relations improves causal discovery over the baseline without weighted relations.

AI-40-标题: Meat Meets Machine! Multiscale Competency Enables Causal Learning

链接: https://arxiv.org/abs/2405.02325
作者: Michael Timothy Bennett
备注: Definitions shared with arXiv:2404.07227, arXiv:2302.00843

点击查看摘要

Abstract:Biological intelligence uses a “multiscale competency architecture” (MCA). It exhibits adaptive, goal directed behaviour at all scales, from cells to organs to organisms. In contrast, machine intelligence is only adaptive and goal directed at a high level. Learned policies are passively interpreted using abstractions (e.g. arithmetic) embodied in static interpreters (e.g. x86). Biological intelligence excels at causal learning. Machine intelligence does not. Previous work showed causal learning follows from weak policy optimisation, which is hindered by presupposed abstractions in silico. Here we formalise MCAs as nested “agentic abstraction layers”, to understand how they might learn causes. We show that weak policy optimisation at low levels enables weak policy optimisation at high. This facilitates what we call “multiscale causal learning” and high level goal directed behaviour. We argue that by engineering human abstractions in silico we disconnect high level goal directed behaviour from the low level goal directed behaviour that gave rise to it. This inhibits causal learning, and we speculate this is one reason why human recall would be accompanied by feeling, and in silico recall not.

AI-41-标题: Combined Compromise for Ideal Solution (CoCoFISo): a multi-criteria decision-making based on the CoCoSo method algorithm

链接: https://arxiv.org/abs/2405.02324
作者: Rôlin Gabriel Rasoanaivo (IRIT, UT Capitole, IRIT-ADRIA), Morteza Yazdani (UIV), Pascale Zaraté (IRIT, UT Capitole, IRIT-ADRIA), Amirhossein Fateh (UPV)
备注: Expert Systems with Applications, 2024

点击查看摘要

Abstract:Each decision-making tool should be tested and validated in real case studies to be practical and fit to global problems. The application of multi-criteria decision-making methods (MCDM) is currently a trend to rank alternatives. In the literature, there are several multi-criteria decision-making methods according to their classification. During our experimentation on the Combined Compromise Solution (CoCoSo) method, we encountered its limits for real cases. The authors examined the applicability of the CoCoFISo method (improved version of combined compromise solution), by a real case study in a university campus and compared the obtained results to other MCDMs such as Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), Weighted Sum Method (WSM) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Our research finding indicates that CoCoSo is an applied method that has been developed to solve complex multi variable assessment problems, while CoCoFISo can improve the shortages observed in CoCoSo and deliver stable outcomes compared to other developed tools. The findings imply that application of CoCoFISo is suggested to decision makers, experts and researchers while they are facing practical challenges and sensitive questions regarding the utilization of a reliable decision-making method. Unlike many prior studies, the current version of CoCoSo is unique, original and is presented for the first time. Its performance was approved using several strategies and examinations.

AI-42-标题: Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment

链接: https://arxiv.org/abs/2405.02321
作者: Mutahira Khalid, Raihana Rahman, Asim Abbas, Sushama Kumari, Iram Wajahat, Syed Ahmad Chan Bukhari
备注: 18 pages, 5 figures

点击查看摘要

Abstract:Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automation and completeness. Despite efforts in automation and the utilization of expert-created ontologies, gaps in connectivity remain prevalent within KGs. In response to these challenges, we propose an innovative approach termed ``Medical Knowledge Graph Automation (M-KGA)". M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, thereby enhancing the completeness of knowledge graphs through the integration of pre-trained embeddings. Our approach introduces two distinct methodologies for uncovering hidden connections within the knowledge graph: a cluster-based approach and a node-based approach. Through rigorous testing involving 100 frequently occurring medical concepts in Electronic Health Records (EHRs), our M-KGA framework demonstrates promising results, indicating its potential to address the limitations of existing knowledge graph automation techniques.

AI-43-标题: A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning

链接: https://arxiv.org/abs/2405.02320
作者: Pengcheng Sun, Erwu Liu, Rui Wang
备注:

点击查看摘要

Abstract:The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.

AI-44-标题: Optimisation challenge for superconducting adiabatic neural network implementing XOR and OR boolean functions

链接: https://arxiv.org/abs/2405.03521
作者: D.S. Pashin, M.V. Bastrakova, D.A. Rybin, I.I. Soloviev, A.E. Schegolev, N.V. Klenov
备注: 13 pages, 12 figures

点击查看摘要

Abstract:In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of the system implementing XOR and OR logical operations.

AI-45-标题: On Probabilistic and Causal Reasoning with Summation Operators

链接: https://arxiv.org/abs/2405.03069
作者: Duligur Ibeling, Thomas F. Icard, Milan Mossé
备注:

点击查看摘要

Abstract:Ibeling et al. (2023). axiomatize increasingly expressive languages of causation and probability, and Mosse et al. (2024) show that reasoning (specifically the satisfiability problem) in each causal language is as difficult, from a computational complexity perspective, as reasoning in its merely probabilistic or “correlational” counterpart. Introducing a summation operator to capture common devices that appear in applications – such as the do -calculus of Pearl (2009) for causal inference, which makes ample use of marginalization – van der Zander et al. (2023) partially extend these earlier complexity results to causal and probabilistic languages with marginalization. We complete this extension, fully characterizing the complexity of probabilistic and causal reasoning with summation, demonstrating that these again remain equally difficult. Surprisingly, allowing free variables for random variable values results in a system that is undecidable, so long as the ranges of these random variables are unrestricted. We finally axiomatize these languages featuring marginalization (or more generally summation), resolving open questions posed by Ibeling et al. (2023).

AI-46-标题: Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi- Agent Simulation Study

链接: https://arxiv.org/abs/2405.02849
作者: Alicia Vidler, Toby Walsh
备注: 13 pages, 3 figures

点击查看摘要

Abstract:Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi-agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a multi-agent simulation approach employing small-scale meta-heuristic methods. This approach aims to represent the opaque bilateral market for Australian government bond trading, capturing the bilateral nature of bank-to-bank trading, also referred to as “over-the-counter” (OTC) trading, and commonly occurring between “market makers”. The uniqueness of the bilateral market, characterized by negotiated transactions and a limited number of agents, yields valuable insights for agent-based modelling and quantitative finance. The inherent rigidity of this market structure, which is at odds with the global proliferation of multilateral platforms and the decentralization of finance, underscores the unique insights offered by our agent-based model. We explore the implications of market rigidity on market structure and consider the element of stability, in market design. This extends the ongoing discourse on complex financial trading environments, providing an enhanced understanding of their dynamics and implications.

AI-47-标题: A Network Simulation of OTC Markets with Multiple Agent s

链接: https://arxiv.org/abs/2405.02480
作者: James T. Wilkinson, Jacob Kelter, John Chen, Uri Wilensky
备注: 20 pages, 17 figures

点击查看摘要

Abstract:We present a novel agent-based approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers and agent visibility is constrained to a network topology. Dynamics, such as changes in price, result from agent-level interactions that ubiquitously occur via market maker agents acting as liquidity providers. Two additional agents are considered: trend investors use a deep convolutional neural network paired with a deep Q-learning framework to inform trading decisions by analysing price history; and value investors use a static price-target to determine their trade directions and sizes. We demonstrate that our novel inclusion of a network topology with market makers facilitates explorations into various market structures. First, we present the model and an overview of its mechanics. Second, we validate our findings via comparison to the real-world: we demonstrate a fat-tailed distribution of price changes, auto-correlated volatility, a skew negatively correlated to market maker positioning, predictable price-history patterns and more. Finally, we demonstrate that our network-based model can lend insights into the effect of market-structure on price-action. For example, we show that markets with sparsely connected intermediaries can have a critical point of fragmentation, beyond which the market forms distinct clusters and arbitrage becomes rapidly possible between the prices of different market makers. A discussion is provided on future work that would be beneficial.

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