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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7,501 to 7,560 of 20,051

Serving DNNs like Clockwork: Performance Predictability from the Bottom Up
Arpan Gujarati, Reza Karimi, Safya Alzayat +4
cs.DCcs.LGarXiv:2006.02464v22020
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Zonghan Wu, Shirui Pan, Guodong Long +3
cs.LGstat.MLarXiv:2005.11650v12020
Universal Differential Equations for Scientific Machine Learning
Christopher Rackauckas, Yingbo Ma, Julius Martensen +6
cs.LGmath.DSq-bio.QMarXiv:2001.04385v42020
Personalized Federated Learning: A Meta-Learning Approach
Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
cs.LGmath.OCstat.MLarXiv:2002.07948v42020
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez, Pierre Boyeau, Nir Yosef +2
stat.MLcs.AIcs.LGarXiv:2002.07217v32020
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Xinyu Fu, Jiani Zhang, Ziqiao Meng +1
cs.SIcs.LGarXiv:2002.01680v22020
Think Locally, Act Globally: Federated Learning with Local and Global Representations
Paul Pu Liang, Terrance Liu, Liu Ziyin +5
cs.LGcs.DCstat.MLarXiv:2001.01523v32020
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
Jamshed Memon, Maira Sami, Rizwan Ahmed Khan
cs.CVcs.LGarXiv:2001.00139v12020
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Bryan Lim, Sercan O. Arik, Nicolas Loeff +1
stat.MLcs.LGarXiv:1912.09363v32019
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
Robin M. Schmidt
cs.LGstat.MLarXiv:1912.05911v12019
Training Deep Learning Models with Norm-Constrained LMOs
Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos +3
cs.LGmath.OCarXiv:2502.07529v22025
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
cs.LGcs.AIcs.ROarXiv:1910.10897v22019
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto +1
cs.LGstat.MLarXiv:1911.08731v22019
Once-for-All: Train One Network and Specialize it for Efficient Deployment
Han Cai, Chuang Gan, Tianzhe Wang +2
cs.LGcs.CVstat.MLarXiv:1908.09791v52019
Hyperbolic Graph Convolutional Neural Networks
Ines Chami, Rex Ying, Christopher Ré +1
cs.LGstat.MLarXiv:1910.12933v12019
Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks
Mehrdad Shafiei Dizaji, Hoda Azari
cs.LGarXiv:2609.01417v12026
Representation Learning: A Review and New Perspectives
Yoshua Bengio, Aaron Courville, Pascal Vincent
cs.LGarXiv:1206.5538v32012
Pseudo-likelihood methods for community detection in large sparse networks
Arash A. Amini, Aiyou Chen, Peter J. Bickel +1
cs.SIcs.LGmath.STarXiv:1207.2340v32012
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

Machine Learning

Serving DNNs like Clockwork: Performance Predictability from the Bottom Up

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Universal Differential Equations for Scientific Machine Learning

Personalized Federated Learning: A Meta-Learning Approach

Decision-Making with Auto-Encoding Variational Bayes

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

Think Locally, Act Globally: Federated Learning with Local and Global Representations

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

Recurrent Neural Networks (RNNs): A gentle Introduction and Overview

Training Deep Learning Models with Norm-Constrained LMOs

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Once-for-All: Train One Network and Specialize it for Efficient Deployment

Hyperbolic Graph Convolutional Neural Networks

Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks

Representation Learning: A Review and New Perspectives

Pseudo-likelihood methods for community detection in large sparse networks

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