Machine Learning

Papers filed under cs.LG on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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5,281 to 5,340 of 20,225

A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
Kejun Huang, Nicholas D. Sidiropoulos, Athanasios P. Liavas
stat.MLcs.LGmath.OCarXiv:1506.04209v22015
Deep Multimodal Subspace Clustering Networks
Mahdi Abavisani, Vishal M. Patel
cs.LGcs.AIcs.CVarXiv:1804.06498v32018
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
cs.LGstat.MLarXiv:1905.12034v12019
A foundation model of vision, audition, and language for in-silico neuroscience
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +5
q-bio.NCcs.LGarXiv:2605.04326v12026
MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance
Jia Xu, Tianyi Wei, Bojian Hou +7
cs.LGcs.AIcs.CLarXiv:2503.13509v22025
Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs
Kyle O'Brien, Stephen Casper, Quentin Anthony +7
cs.LGcs.AIarXiv:2508.06601v22025
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
Hongliang Lu, Zhonglin Xie, Yaoyu Wu +3
cs.AIcs.LGarXiv:2502.11102v22025
D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data
Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang +1
cs.LGarXiv:2609.01802v12026
When Do Larger Batches Help Scale LLM Reinforcement Learning?
Ziniu Li, Jinbo Wang, Guanhua Huang +3
cs.LGcs.AIarXiv:2608.29296v12026
Blink: Fast and Generic Collectives for Distributed ML
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee +3
cs.DCcs.LGarXiv:1910.04940v12019
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
Ali Behrouz, Meisam Razaviyayn, Peilin Zhong +1
cs.LGcs.AIarXiv:2504.13173v12025
The Emergence of Spectral Universality in Deep Networks
Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli
stat.MLcs.LGarXiv:1802.09979v12018
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Víctor Campos, Alexander Trott, Caiming Xiong +3
cs.LGcs.AIstat.MLarXiv:2002.03647v42020
Recurrent Pixel Embedding for Instance Grouping
Shu Kong, Charless Fowlkes
cs.CVcs.LGcs.MMarXiv:1712.08273v12017
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification
Simon Graham, Mostafa Jahanifar, Ayesha Azam +14
cs.CVcs.LGarXiv:2108.11195v22021
SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM
Xiaojiang Zhang, Jinghui Wang, Zifei Cheng +14
cs.LGarXiv:2504.14286v22025
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsum Yuta Kawachi +1
stat.MLcs.LGcs.SDarXiv:1810.09133v12018
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Sujay Khandagale, Han Xiao, Rohit Babbar
cs.LGstat.MLarXiv:1904.08249v22019
Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets
Summaiya Unnisa Begum, Mohammed Nadeem Ullah, Mohammed Abdul Ghani Khan
cs.LGarXiv:2609.01765v12026
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Xiangfei Qiu, Zhe Li, Wanghui Qiu +10
cs.LGarXiv:2506.18046v22025
Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, Gerome Miklau
cs.LGcs.CRstat.MLarXiv:1901.09136v12019
Provably Consistent Partial-Label Learning
Lei Feng, Jiaqi Lv, Bo Han +5
cs.LGstat.MLarXiv:2007.08929v22020
Representation Learning for Tabular Data: A Comprehensive Survey
Jun-Peng Jiang, Si-Yang Liu, Hao-Run Cai +2
cs.LGarXiv:2504.16109v12025
Interpretable Symptom Vectors for Depression in a Large Language Model
Fangyi Zhu, Ajay Subramanian, Allison Constant +3
cs.CLcs.AIcs.LGarXiv:2609.01832v12026
Trading Inference-Time Compute for Adversarial Robustness
Wojciech Zaremba, Evgenia Nitishinskaya, Boaz Barak +8
cs.LGcs.CRarXiv:2501.18841v12025
Forgetting Transformer: Softmax Attention with a Forget Gate
Zhixuan Lin, Evgenii Nikishin, Xu Owen He +1
cs.LGcs.AIcs.CLarXiv:2503.02130v22025
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Shenda Hong, Yuxi Zhou, Junyuan Shang +2
eess.SPcs.CVcs.LGarXiv:2001.01550v32019
A Survey on Deep Learning for Skin Lesion Segmentation
Zahra Mirikharaji, Kumar Abhishek, Alceu Bissoto +5
eess.IVcs.CVcs.LGarXiv:2206.00356v32022
RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization
Songming Liu, Bangguo Li, Kai Ma +5
cs.ROcs.AIcs.CVarXiv:2602.03310v12026
Indoor occupancy estimation from carbon dioxide concentration
Chaoyang Jiang, Mustafa K. Masood, Yeng Chai Soh +1
eess.SYcs.LGarXiv:1607.05962v12016
Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model
Jiarui Jin, Haoyu Wang, Hongyan Li +3
cs.LGcs.AIarXiv:2502.10707v12025
TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
Yizhi Li, Qingshui Gu, Zhoufutu Wen +14
cs.LGcs.CLarXiv:2508.17445v12025
Practical Efficiency of Muon for Pretraining
Essential AI, :, Ishaan Shah +22
cs.LGstat.MLarXiv:2505.02222v42025
SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
cs.LGcs.AIcs.CLarXiv:2506.09108v12025
Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier
Xinpeng Li, Kai Ming Ting
cs.LGcs.AIarXiv:2511.09332v12025
SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
Jintao Zhang, Jia Wei, Pengle Zhang +6
cs.LGcs.AIcs.ARarXiv:2505.11594v32025
Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation
Sebastien Tchitchek, Julien Tierny
cs.CGcs.LGarXiv:2609.01528v12026
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
Yongxin Liu, Jian Wang, Jianqiang Li +2
cs.CRcs.AIcs.LGarXiv:2101.10181v12021
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko +1
cs.CLcs.AIcs.LGarXiv:2305.15852v32023
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
Wenzhe Li, Yong Lin, Mengzhou Xia +1
cs.CLcs.LGarXiv:2502.00674v12025
Flow Navigation by Smart Microswimmers via Reinforcement Learning
Simona Colabrese, Kristian Gustavsson, Antonio Celani +1
physics.flu-dyncond-mat.stat-mechcs.LGarXiv:1701.08848v32017
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Jakub Macina, Nico Daheim, Ido Hakimi +3
cs.CLcs.AIcs.LGarXiv:2502.18940v22025
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Wu, Xingliang Yuan +3
cs.LGcs.AIarXiv:2205.07424v22022
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
Yifan Wang, Suyao Tang, Yuntong Lei +3
cs.IRcs.LGcs.SIarXiv:2106.10879v12021
Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?
Chenrui Fan, Ming Li, Lichao Sun +1
cs.AIcs.CLcs.LGarXiv:2504.06514v22025
An overview on deep learning-based approximation methods for partial differential equations
Christian Beck, Martin Hutzenthaler, Arnulf Jentzen +1
math.NAcs.LGarXiv:2012.12348v32020
Edge-Girth as a Structural Edge Feature for Graph Neural Networks
Lilian Marey, Charlotte Laclau
cs.LGarXiv:2609.01441v12026
Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Zihe Liu, Jiashun Liu, Yancheng He +13
cs.LGcs.CLarXiv:2508.08221v32025
Position: Privacy Is a Claim, Not a Property of Synthetic Data
Jiachen Zhao, Antonia Januszewicz, Taeho Jung
cs.LGcs.CRarXiv:2609.01273v12026
Emotion in Reinforcement Learning Agents and Robots: A Survey
Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
cs.LGcs.AIcs.HCarXiv:1705.05172v12017
The Constitutional Coverage Trilemma in AI Governance
Natalija Mitic, Soona Sedahmed A. O., Mamadou Selly Ly +1
cs.LGcs.AIarXiv:2609.01275v12026
Tiresias: Predicting Security Events Through Deep Learning
Yun Shen, Enrico Mariconti, Pierre-Antoine Vervier +1
cs.CRcs.LGarXiv:1905.10328v12019
Fairwashing: the risk of rationalization
Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau +3
cs.LGstat.MLarXiv:1901.09749v32019
From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs
Jie Chen, Xiangqian Yu, Yanchao Lian +9
cs.IRcs.AIcs.LGarXiv:2609.01240v12026
Learning Sparse Decision Trees via Transformer Variational Auto-Encoders
Giacomo Fidone, Alessio Cascione, Riccardo Guidotti
cs.LGcs.AIarXiv:2609.01430v12026
UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs
Yufei He, Yuan Sui, Xiaoxin He +3
cs.LGarXiv:2502.00806v22025
Insights into LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi
cs.LGstat.MLarXiv:1902.10756v32019
When Does Online Adaptation Pay on the Edge? A Leakage-Free Evaluation of Warmup, Learning-Rate Selection, and Resource Trade-offs for Time-Series Forecasting
Takumi Fujimoto, Hiroaki Nishi
cs.LGarXiv:2609.01126v12026
Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
Kieran Didi, Zuobai Zhang, Guoqing Zhou +11
cs.LGarXiv:2603.27950v12026
Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials
Pingbing Ming, Han Wang
cs.LGmath-phphysics.chem-pharXiv:2609.00528v22026

Machine Learning

A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization

Deep Multimodal Subspace Clustering Networks

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

A foundation model of vision, audition, and language for in-silico neuroscience

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs

OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling

D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data

When Do Larger Batches Help Scale LLM Reinforcement Learning?

Blink: Fast and Generic Collectives for Distributed ML

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization

The Emergence of Spectral Universality in Deep Networks

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Recurrent Pixel Embedding for Instance Grouping

Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification

SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM

Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma

Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification

Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

Graphical-model based estimation and inference for differential privacy

Provably Consistent Partial-Label Learning

Representation Learning for Tabular Data: A Comprehensive Survey

Interpretable Symptom Vectors for Depression in a Large Language Model

Trading Inference-Time Compute for Adversarial Robustness

Forgetting Transformer: Softmax Attention with a Forget Gate

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

A Survey on Deep Learning for Skin Lesion Segmentation

RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization

Indoor occupancy estimation from carbon dioxide concentration

Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model

TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling

Practical Efficiency of Muon for Pretraining

SensorLM: Learning the Language of Wearable Sensors

Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier

SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training

Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation

Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation

Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Flow Navigation by Smart Microswimmers via Reinforcement Learning

MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation

Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?

An overview on deep learning-based approximation methods for partial differential equations

Edge-Girth as a Structural Edge Feature for Graph Neural Networks

Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning

Position: Privacy Is a Claim, Not a Property of Synthetic Data

Emotion in Reinforcement Learning Agents and Robots: A Survey

The Constitutional Coverage Trilemma in AI Governance

Tiresias: Predicting Security Events Through Deep Learning

Fairwashing: the risk of rationalization

From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs

Learning Sparse Decision Trees via Transformer Variational Auto-Encoders

UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs

Insights into LSTM Fully Convolutional Networks for Time Series Classification

When Does Online Adaptation Pay on the Edge? A Leakage-Free Evaluation of Warmup, Learning-Rate Selection, and Resource Trade-offs for Time-Series Forecasting

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials