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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7,621 to 7,680 of 20,153

Tighter Theory for Local SGD on Identical and Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
cs.LGcs.DCmath.NAarXiv:1909.04746v42019
On the Variance of the Adaptive Learning Rate and Beyond
Liyuan Liu, Haoming Jiang, Pengcheng He +4
cs.LGcs.CLstat.MLarXiv:1908.03265v42019
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
cs.LGstat.MLarXiv:1907.04931v42019
Large Scale Adversarial Representation Learning
Jeff Donahue, Karen Simonyan
cs.CVcs.LGstat.MLarXiv:1907.02544v22019
On the Convergence of FedAvg on Non-IID Data
Xiang Li, Kaixuan Huang, Wenhao Yang +2
stat.MLcs.LGmath.OCarXiv:1907.02189v42019
Does Learning Require Memorization? A Short Tale about a Long Tail
Vitaly Feldman
cs.LGstat.MLarXiv:1906.05271v42019
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Simon S. Du, Kangcheng Hou, Barnabás Póczos +3
cs.LGcs.AIcs.CVarXiv:1905.13192v22019
Text Classification Algorithms: A Survey
Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa +3
cs.LGcs.AIcs.CLarXiv:1904.08067v52019
Provably Powerful Graph Networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky +1
cs.LGstat.MLarXiv:1905.11136v42019
Deep Reinforcement Learning for Sepsis Treatment
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed +3
cs.AIcs.LGarXiv:1711.09602v12017
Mercury: Ultra-Fast Language Models Based on Diffusion
Inception Labs, Samar Khanna, Siddhant Kharbanda +10
cs.CLcs.AIcs.LGarXiv:2506.17298v12025
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora, Simon S. Du, Wei Hu +3
cs.LGcs.CVcs.NEarXiv:1904.11955v22019
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck
cs.IRcs.LGstat.MLarXiv:1905.03375v12019
CopyShield: A Cross-Level Benchmark of Copyright Defenses in LLMs
Maryam Alshehyari, Dushyant Singh Chauhan, Samuele Poppi +3
cs.LGarXiv:2609.01161v12026
On the Convergence of Adam and Beyond
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
cs.LGmath.OCstat.MLarXiv:1904.09237v12019
A Survey on Traffic Signal Control Methods
Hua Wei, Guanjie Zheng, Vikash Gayah +1
cs.LGcs.AIstat.MLarXiv:1904.08117v32019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset +1
math.STcs.LGstat.MLarXiv:1903.08560v52019
Three scenarios for continual learning
Gido M. van de Ven, Andreas S. Tolias
cs.LGcs.AIcs.CVarXiv:1904.07734v12019
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
Luca Pion-Tonachini, Ken Kreutz-Delgado, Scott Makeig
eess.SPcs.LGstat.MLarXiv:1901.07915v22019
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank Reddi +7
cs.LGcs.AIcs.CLarXiv:1904.00962v52019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz +4
stat.MLcs.LGarXiv:1902.06720v42019
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

Machine Learning

Tighter Theory for Local SGD on Identical and Heterogeneous Data

On the Variance of the Adaptive Learning Rate and Beyond

GraphSAINT: Graph Sampling Based Inductive Learning Method

Large Scale Adversarial Representation Learning

On the Convergence of FedAvg on Non-IID Data

Does Learning Require Memorization? A Short Tale about a Long Tail

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

Text Classification Algorithms: A Survey

Provably Powerful Graph Networks

Deep Reinforcement Learning for Sepsis Treatment

Mercury: Ultra-Fast Language Models Based on Diffusion

On Exact Computation with an Infinitely Wide Neural Net

Embarrassingly Shallow Autoencoders for Sparse Data

CopyShield: A Cross-Level Benchmark of Copyright Defenses in LLMs

On the Convergence of Adam and Beyond

A Survey on Traffic Signal Control Methods

Surprises in High-Dimensional Ridgeless Least Squares Interpolation

Three scenarios for continual learning

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

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