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)

7,261 to 7,320 of 20,193

Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda +5
hep-latcond-mat.stat-mechcs.LGarXiv:2003.06413v12020
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
cs.LGcs.CRcs.DCarXiv:2002.04156v32020
Can Graph Neural Networks Count Substructures?
Zhengdao Chen, Lei Chen, Soledad Villar +1
cs.LGcs.DMstat.MLarXiv:2002.04025v42020
Naive Exploration is Optimal for Online LQR
Max Simchowitz, Dylan J. Foster
cs.LGmath.OCstat.MLarXiv:2001.09576v42020
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan +7
cs.LGstat.MLarXiv:2001.08361v12020
Deep Reinforcement Learning for Trading
Zihao Zhang, Stefan Zohren, Stephen Roberts
q-fin.CPcs.LGq-fin.TRarXiv:1911.10107v12019
General $E(2)$-Equivariant Steerable CNNs
Maurice Weiler, Gabriele Cesa
cs.CVcs.LGeess.IVarXiv:1911.08251v22019
A CNN-RNN Framework for Crop Yield Prediction
Saeed Khaki, Lizhi Wang, Sotirios V. Archontoulis
cs.LGq-bio.QMstat.MLarXiv:1911.09045v22019
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things
Maede Zolanvari, Marcio A. Teixeira, Lav Gupta +2
cs.CRcs.LGcs.NIarXiv:1911.05771v12019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts +6
cs.LGcs.CLstat.MLarXiv:1910.10683v42019
Energy Efficient Federated Learning Over Wireless Communication Networks
Zhaohui Yang, Mingzhe Chen, Walid Saad +2
cs.ITcs.LGstat.MLarXiv:1911.02417v22019
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Wentai Wu, Ligang He, Weiwei Lin +3
cs.DCcs.LGarXiv:1910.01355v42019
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls +4
cs.LGcs.DCstat.MLarXiv:1909.12326v52019
Distributionally Robust Optimization: A Review
Hamed Rahimian, Sanjay Mehrotra
math.OCcs.LGstat.MLarXiv:1908.05659v12019
A review on Deep Reinforcement Learning for Fluid Mechanics
Paul Garnier, Jonathan Viquerat, Jean Rabault +3
physics.comp-phcs.LGphysics.flu-dynarXiv:1908.04127v22019
On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein +2
cs.LGstat.MLarXiv:1907.13625v22019
Multi-task Self-Supervised Learning for Human Activity Detection
Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien
cs.LGstat.MLarXiv:1907.11879v12019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
cs.LGcs.NEstat.MLarXiv:1906.05890v42019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
Nicholas Geneva, Nicholas Zabaras
physics.comp-phcs.LGstat.MLarXiv:1906.05747v32019
Hierarchical Reasoning Model
Guan Wang, Jin Li, Yuhao Sun +6
cs.AIcs.LGarXiv:2506.21734v32025
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
cs.CYcs.AIcs.LGarXiv:1906.05433v22019
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
Zhouchen Lin, Risheng Liu, Huan Li
math.NAcs.LGmath.OCarXiv:1310.5035v22013
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Difan Zou, Quanquan Gu
cs.LGmath.OCstat.MLarXiv:1906.04688v12019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Hadi Salman, Greg Yang, Jerry Li +4
cs.LGcs.CRstat.MLarXiv:1906.04584v52019
Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System
Syed Ali Raza Shah, Biju Issac
cs.NIcs.CRcs.LGarXiv:1710.04843v22017
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
Harald Bayerlein, Mirco Theile, Marco Caccamo +1
cs.MAcs.ITcs.LGarXiv:2010.12461v32020
Cormorant: Covariant Molecular Neural Networks
Brandon Anderson, Truong-Son Hy, Risi Kondor
physics.comp-phcs.LGstat.MLarXiv:1906.04015v32019
An Introduction to Variational Autoencoders
Diederik P. Kingma, Max Welling
cs.LGstat.MLarXiv:1906.02691v32019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
stat.MLcs.LGphysics.chem-pharXiv:1906.00957v32019
Comparison of non-linear activation functions for deep neural networks on MNIST classification task
Dabal Pedamonti
cs.LGstat.MLarXiv:1804.02763v12018
Why gradient clipping accelerates training: A theoretical justification for adaptivity
Jingzhao Zhang, Tianxing He, Suvrit Sra +1
math.OCcs.LGarXiv:1905.11881v22019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Yuan Cao, Quanquan Gu
cs.LGmath.OCstat.MLarXiv:1905.13210v32019
Improved Precision and Recall Metric for Assessing Generative Models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine +2
stat.MLcs.LGcs.NEarXiv:1904.06991v32019
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
Chao Ning, Fengqi You
cs.LGmath.OCstat.MLarXiv:1904.01934v12019
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
Yawei Luo, Ping Liu, Tao Guan +2
cs.CVcs.AIcs.LGarXiv:1904.00876v12019
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Abdelrahim Kasem Ahmad, Assef Jafar, Kadan Aljoumaa
cs.CYcs.DCcs.LGarXiv:1904.00690v12019
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin +8
cs.LGstat.MLarXiv:1903.03096v42019
A new Backdoor Attack in CNNs by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, Benedetta Tondi
cs.CRcs.CVcs.LGarXiv:1902.11237v12019
Flow Q-Learning
Seohong Park, Qiyang Li, Sergey Levine
cs.LGcs.AIarXiv:2502.02538v22025
Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity
Lei Wang, Jieming Bian, Letian Zhang +1
cs.LGcs.AIarXiv:2609.00632v12026
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Daewoo Kim, Sangwoo Moon, David Hostallero +4
cs.AIcs.LGcs.MAarXiv:1902.01554v12019
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
Amy Zhang, Nicolas Ballas, Joelle Pineau
cs.LGcs.AIstat.MLarXiv:1806.07937v22018
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
Meixin Zhu, Xuesong Wang, Yinhai Wang
cs.LGcs.AIstat.MLarXiv:1901.00569v12019
FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review
Ahmad Shawahna, Sadiq M. Sait, Aiman El-Maleh
cs.NEcs.ARcs.CVarXiv:1901.00121v12019
AIDE: AI-Driven Exploration in the Space of Code
Zhengyao Jiang, Dominik Schmidt, Dhruv Srikanth +4
cs.AIcs.LGarXiv:2502.13138v12025
Federated Learning via Over-the-Air Computation
Kai Yang, Tao Jiang, Yuanming Shi +1
cs.LGcs.ITeess.SParXiv:1812.11750v32018
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé +2
cs.HCcs.CYcs.LGarXiv:1812.05239v22018
An Introduction to Deep Reinforcement Learning
Vincent Francois-Lavet, Peter Henderson, Riashat Islam +2
cs.LGcs.AIstat.MLarXiv:1811.12560v22018
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Xinyi Wang, Wanrong Zhu, Michael Saxon +2
cs.CLcs.AIcs.LGarXiv:2301.11916v42023
Learning Models with Uniform Performance via Distributionally Robust Optimization
John Duchi, Hongseok Namkoong
stat.MLcs.LGarXiv:1810.08750v62018
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Ingoo Lee, Jongsoo Keum, Hojung Nam
q-bio.QMcs.LGarXiv:1811.02114v12018
Automated software vulnerability detection with machine learning
Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell +13
cs.SEcs.LGstat.MLarXiv:1803.04497v22018
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Yuanming Hu, Jiancheng Liu, Andrew Spielberg +5
cs.ROcs.AIcs.GRarXiv:1810.01054v12018
Federated Quantum Machine Learning
Samuel Yen-Chi Chen, Shinjae Yoo
quant-phcs.AIcs.CRarXiv:2103.12010v12021
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo, Yongyi Mao, Richong Zhang
cs.LGcs.AIstat.MLarXiv:1809.02499v32018
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
Zonghao Huang, Rui Hu, Yuanxiong Guo +2
cs.LGstat.MLarXiv:1808.10101v62018
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli +2
cs.LGcs.CRstat.MLarXiv:1811.02054v62018
Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
Tomek Korbak, Mikita Balesni, Elizabeth Barnes +38
cs.AIcs.LGstat.MLarXiv:2507.11473v22025
ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
Robert Tjarko Lange, Yuki Imajuku, Edoardo Cetin
cs.CLcs.LGarXiv:2509.19349v12025
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
cs.LGmath.OCstat.MLarXiv:1011.0097v12010

Machine Learning

Equivariant flow-based sampling for lattice gauge theory

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Can Graph Neural Networks Count Substructures?

Naive Exploration is Optimal for Online LQR

Scaling Laws for Neural Language Models

Deep Reinforcement Learning for Trading

General $E(2)$-Equivariant Steerable CNNs

A CNN-RNN Framework for Crop Yield Prediction

Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Energy Efficient Federated Learning Over Wireless Communication Networks

SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead

Model Pruning Enables Efficient Federated Learning on Edge Devices

Distributionally Robust Optimization: A Review

A review on Deep Reinforcement Learning for Fluid Mechanics

On Mutual Information Maximization for Representation Learning

Multi-task Self-Supervised Learning for Human Activity Detection

Gradient Descent Maximizes the Margin of Homogeneous Neural Networks

Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks

Hierarchical Reasoning Model

Tackling Climate Change with Machine Learning

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

An Improved Analysis of Training Over-parameterized Deep Neural Networks

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System

Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning

Cormorant: Covariant Molecular Neural Networks

An Introduction to Variational Autoencoders

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

Comparison of non-linear activation functions for deep neural networks on MNIST classification task

Why gradient clipping accelerates training: A theoretical justification for adaptivity

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

Improved Precision and Recall Metric for Assessing Generative Models

Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming

Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

A new Backdoor Attack in CNNs by training set corruption without label poisoning

Flow Q-Learning

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

Learning to Schedule Communication in Multi-agent Reinforcement Learning

A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning

FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review

AIDE: AI-Driven Exploration in the Space of Code

Federated Learning via Over-the-Air Computation

Improving fairness in machine learning systems: What do industry practitioners need?

An Introduction to Deep Reinforcement Learning

Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Learning Models with Uniform Performance via Distributionally Robust Optimization

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Automated software vulnerability detection with machine learning

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Federated Quantum Machine Learning

MixUp as Locally Linear Out-Of-Manifold Regularization

DP-ADMM: ADMM-based Distributed Learning with Differential Privacy

Exploring Connections Between Active Learning and Model Extraction

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution

Sparse Inverse Covariance Selection via Alternating Linearization Methods