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.

Search paper metadata (including unsummarized papers)

7,681 to 7,740 of 20,192

Graph Neural Networks for Social Recommendation
Wenqi Fan, Yao Ma, Qing Li +4
cs.IRcs.LGcs.SIarXiv:1902.07243v22019
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Liangchen Luo, Yuanhao Xiong, Yan Liu +1
cs.LGstat.MLarXiv:1902.09843v12019
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao +3
cs.LGstat.MLarXiv:1901.08573v32019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1
physics.comp-phcs.CVcs.LGarXiv:1901.06314v12019
Hybrid Recommender Systems: A Systematic Literature Review
Erion Çano, Maurizio Morisio
cs.IRcs.CYcs.LGarXiv:1901.03888v12019
A Survey of Unsupervised Deep Domain Adaptation
Garrett Wilson, Diane J. Cook
cs.LGstat.MLarXiv:1812.02849v32018
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
cs.LGcs.AIcs.ROarXiv:1812.05905v22018
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati +3
cs.LGcs.GTmath.OCarXiv:1807.02629v22018
Deep Neural Networks for Estimation and Inference
Max H. Farrell, Tengyuan Liang, Sanjog Misra
econ.EMcs.LGmath.STarXiv:1809.09953v32018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel +2
cs.LGstat.MLarXiv:1809.11165v62018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos +1
cs.LGmath.OCstat.MLarXiv:1810.02054v22018
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon +4
stat.MLcs.LGarXiv:1808.06670v52018
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Hao Yu, Sen Yang, Shenghuo Zhu
math.OCcs.DCcs.LGarXiv:1807.06629v32018
The relativistic discriminator: a key element missing from standard GAN
Alexia Jolicoeur-Martineau
cs.LGcs.AIcs.CRarXiv:1807.00734v32018
Understanding Batch Normalization
Johan Bjorck, Carla Gomes, Bart Selman +1
cs.LGcs.AIstat.MLarXiv:1806.02375v42018
DARTS: Differentiable Architecture Search
Hanxiao Liu, Karen Simonyan, Yiming Yang
cs.LGcs.CLcs.CVarXiv:1806.09055v22018
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Raghav Singhal, Zachary Horvitz, Ryan Teehan +4
cs.LGcs.CLcs.CVarXiv:2501.06848v52025
Robustness May Be at Odds with Accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom +2
stat.MLcs.CVcs.LGarXiv:1805.12152v52018
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste
cs.LGcs.AIcs.CVarXiv:1805.10123v42018
Hyperbolic Neural Networks
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann
cs.LGstat.MLarXiv:1805.09112v22018
Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee +1
cs.LGcs.AIstat.MLarXiv:1805.08296v42018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin, Yudong Chen, Kannan Ramchandran +1
cs.LGcs.CRcs.DCarXiv:1803.01498v22018
Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov +3
stat.MLcs.CRcs.LGarXiv:1802.08908v12018
Mean Field Multi-Agent Reinforcement Learning
Yaodong Yang, Rui Luo, Minne Li +3
cs.MAcs.AIcs.LGarXiv:1802.05438v52018
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Yinhao Zhu, Nicholas Zabaras
physics.comp-phcs.CVcs.LGarXiv:1801.06879v12018
Reasoning with Latent Thoughts: On the Power of Looped Transformers
Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li +2
cs.CLcs.AIcs.LGarXiv:2502.17416v12025
Demystifying MMD GANs
Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel +1
stat.MLcs.LGarXiv:1801.01401v52018
Size-Independent Sample Complexity of Neural Networks
Noah Golowich, Alexander Rakhlin, Ohad Shamir
cs.LGcs.NEstat.MLarXiv:1712.06541v52017
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
Xingjian Leng, Jaskirat Singh, Yunzhong Hou +3
cs.CVcs.LGarXiv:2504.10483v32025
Towards Accurate Binary Convolutional Neural Network
Xiaofan Lin, Cong Zhao, Wei Pan
cs.LGstat.MLarXiv:1711.11294v12017
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari
cs.AIcs.LGstat.MLarXiv:1711.07970v22017
Weakly-Supervised Neural Text Classification
Yu Meng, Jiaming Shen, Chao Zhang +1
cs.IRcs.CLcs.LGarXiv:1809.01478v22018
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition
Chris Donahue, Bo Li, Rohit Prabhavalkar
cs.SDcs.LGcs.NEarXiv:1711.05747v22017
The Implicit Bias of Gradient Descent on Separable Data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson +2
stat.MLcs.LGarXiv:1710.10345v72017
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante +8
cs.CVcs.AIcs.LGarXiv:1711.01468v12017
Self-Normalizing Neural Networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr +1
cs.LGstat.MLarXiv:1706.02515v52017
A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski
cs.CVcs.AIcs.LGarXiv:1710.05381v22017
A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni +2
cs.LGarXiv:1707.02038v32017
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang +2
physics.comp-phcs.LGphysics.chem-pharXiv:1707.09571v22017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang, Yixuan Li, R. Srikant
cs.LGstat.MLarXiv:1706.02690v52017
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Balázs Hidasi, Alexandros Karatzoglou
cs.LGarXiv:1706.03847v32017
A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform
Thai T. Pham, Yuanyuan Shen
stat.MLcs.IRcs.LGarXiv:1706.02795v12017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern +2
stat.MLcs.LGarXiv:1705.08292v22017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen, Cong Xu, Feng Yan +4
cs.LGcs.DCcs.NEarXiv:1705.07878v62017
Hierarchical Clustering: Objective Functions and Algorithms
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn +1
cs.DScs.LGarXiv:1704.02147v12017
Minimax Regret Bounds for Reinforcement Learning
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
stat.MLcs.AIcs.LGarXiv:1703.05449v22017
Tensor SVD: Statistical and Computational Limits
Anru Zhang, Dong Xia
math.STcs.LGstat.MEarXiv:1703.02724v42017
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L. Bartlett, Nick Harvey, Chris Liaw +1
cs.LGarXiv:1703.02930v32017
Simple, Efficient, and Neural Algorithms for Sparse Coding
Sanjeev Arora, Rong Ge, Tengyu Ma +1
cs.LGcs.DScs.NEarXiv:1503.00778v12015
Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
cs.LGcs.DScs.ITarXiv:1703.00893v42017
How to Escape Saddle Points Efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli +2
cs.LGmath.OCstat.MLarXiv:1703.00887v12017
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang +2
cs.LGcs.NEstat.MLarXiv:1703.00573v52017
A geometric analysis of subspace clustering with outliers
Mahdi Soltanolkotabi, Emmanuel J. Candés
cs.ITcs.LGmath.STarXiv:1112.4258v52011
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu +1
cs.AIcs.LGstat.MLarXiv:1702.08892v32017
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, Yee Whye Teh
cs.LGstat.MLarXiv:1611.00712v32016
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
Léo Grinsztajn, Klemens Flöge, Oscar Key +23
cs.LGstat.MLarXiv:2511.08667v22025
Energy-based Generative Adversarial Network
Junbo Zhao, Michael Mathieu, Yann LeCun
cs.LGstat.MLarXiv:1609.03126v42016
Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication
Dana Hughes, Nikolaus Correll
cs.LGcs.ROarXiv:1606.03508v12016
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
Hongli Yu, Tinghong Chen, Jiangtao Feng +8
cs.CLcs.AIcs.LGarXiv:2507.02259v22025
An Actor-Critic Algorithm for Sequence Prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu +5
cs.LGarXiv:1607.07086v32016

Machine Learning

Graph Neural Networks for Social Recommendation

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Theoretically Principled Trade-off between Robustness and Accuracy

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

Hybrid Recommender Systems: A Systematic Literature Review

A Survey of Unsupervised Deep Domain Adaptation

Soft Actor-Critic Algorithms and Applications

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

Deep Neural Networks for Estimation and Inference

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Learning deep representations by mutual information estimation and maximization

Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

The relativistic discriminator: a key element missing from standard GAN

Understanding Batch Normalization

DARTS: Differentiable Architecture Search

A General Framework for Inference-time Scaling and Steering of Diffusion Models

Robustness May Be at Odds with Accuracy

TADAM: Task dependent adaptive metric for improved few-shot learning

Hyperbolic Neural Networks

Data-Efficient Hierarchical Reinforcement Learning

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Scalable Private Learning with PATE

Mean Field Multi-Agent Reinforcement Learning

Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification

Reasoning with Latent Thoughts: On the Power of Looped Transformers

Demystifying MMD GANs

Size-Independent Sample Complexity of Neural Networks

REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers

Towards Accurate Binary Convolutional Neural Network

Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge

Weakly-Supervised Neural Text Classification

Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition

The Implicit Bias of Gradient Descent on Separable Data

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Self-Normalizing Neural Networks

A systematic study of the class imbalance problem in convolutional neural networks

A Tutorial on Thompson Sampling

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform

The Marginal Value of Adaptive Gradient Methods in Machine Learning

TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

Hierarchical Clustering: Objective Functions and Algorithms

Minimax Regret Bounds for Reinforcement Learning

Tensor SVD: Statistical and Computational Limits

Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks

Simple, Efficient, and Neural Algorithms for Sparse Coding

Being Robust (in High Dimensions) Can Be Practical

How to Escape Saddle Points Efficiently

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

A geometric analysis of subspace clustering with outliers

Bridging the Gap Between Value and Policy Based Reinforcement Learning

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models

Energy-based Generative Adversarial Network

Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

An Actor-Critic Algorithm for Sequence Prediction