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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18,721 to 18,780 of 20,221

Improving Factuality and Reasoning in Language Models through Multiagent Debate
Yilun Du, Shuang Li, Antonio Torralba +2
cs.CLcs.AIcs.CVarXiv:2305.14325v12023
Hyperparameters and Tuning Strategies for Random Forest
Philipp Probst, Marvin Wright, Anne-Laure Boulesteix
stat.MLcs.LGarXiv:1804.03515v22018
Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
cs.LGcs.CYstat.MLarXiv:1609.05807v22016
When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
Ding Zhang, Runtao Zhou, Wenqing Zheng +3
cs.LGarXiv:2606.03712v12026
Physics-informed neural networks (PINNs) for fluid mechanics: A review
Shengze Cai, Zhiping Mao, Zhicheng Wang +2
physics.flu-dyncs.LGarXiv:2105.09506v12021
Sequence Transduction with Recurrent Neural Networks
Alex Graves
cs.NEcs.LGstat.MLarXiv:1211.3711v12012
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt +3
cs.LGcs.MAstat.MLarXiv:1803.11485v22018
Qwen-Image-Flash: Beyond Objective Design
Tianhe Wu, Kun Yan, Zikai Zhou +21
cs.CVcs.AIcs.GRarXiv:2606.03746v22026
SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction
Dan Jacobellis, Neeraja J. Yadwadkar
eess.IVcs.CVcs.LGarXiv:2606.03940v12026
Hypergraph Neural Networks
Yifan Feng, Haoxuan You, Zizhao Zhang +2
cs.LGstat.MLarXiv:1809.09401v32018
Self-critical Sequence Training for Image Captioning
Steven J. Rennie, Etienne Marcheret, Youssef Mroueh +2
cs.LGcs.AIcs.CVarXiv:1612.00563v22016
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Rishit Dagli, Abir Harrasse, Luke Zhang +4
cs.LGcs.CLarXiv:2606.05165v12026
Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion
Matiur Rahman Minar, Seunghun Oh, GangHyeon Jeong +1
cs.CVcs.AIcs.LGarXiv:2606.14732v12026
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Tim Salimans, Diederik P. Kingma
cs.LGcs.AIcs.NEarXiv:1602.07868v32016
Voyager: An Open-Ended Embodied Agent with Large Language Models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang +5
cs.AIcs.LGarXiv:2305.16291v22023
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann
cs.LGstat.MLarXiv:1810.05997v62018
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, Doina Precup
cs.LGcs.AIstat.MLarXiv:1812.02900v32018
Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
Ruida Wang, Jerry Huang, Pengcheng Wang +3
cs.AIcs.LGcs.LOarXiv:2606.06523v22026
Modeling Tabular data using Conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante +1
cs.LGstat.MLarXiv:1907.00503v22019
Reconciling modern machine learning practice and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma +1
stat.MLcs.LGarXiv:1812.11118v22018
Make-A-Video: Text-to-Video Generation without Text-Video Data
Uriel Singer, Adam Polyak, Thomas Hayes +10
cs.CVcs.AIcs.LGarXiv:2209.14792v12022
Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO
Yiran Xu, Yiming Ren, Zicheng Lin +8
cs.LGcs.AIarXiv:2605.30789v32026
GLU Variants Improve Transformer
Noam Shazeer
cs.LGcs.NEstat.MLarXiv:2002.05202v12020
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, Surya Ganguli
cs.NEcond-mat.dis-nncs.CVarXiv:1312.6120v32013
Stateful Visual Encoders for Vision-Language Models
Zirui Wang, Junwei Yu, Adam Yala +3
cs.CVcs.CLcs.LGarXiv:2606.04433v12026
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras +3
stat.MLcs.CRcs.CVarXiv:1905.02175v42019
Representation Learning on Graphs: Methods and Applications
William L. Hamilton, Rex Ying, Jure Leskovec
cs.SIcs.LGarXiv:1709.05584v32017
Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi +1
cs.LGstat.MLarXiv:1705.10467v22017
Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms
Chong Zhang, Xiang Li, Jia Wang +2
cs.LGcs.CVarXiv:2606.04767v12026
TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration
Soyeong Jeong, Jinheon Baek, Minki Kang +1
cs.CLcs.AIcs.LGarXiv:2606.04743v22026
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu +1
stat.MLcs.LGarXiv:1505.05424v22015
Designing Network Design Spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick +2
cs.CVcs.LGarXiv:2003.13678v12020
Self-supervised Learning: Generative or Contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou +4
cs.LGstat.MLarXiv:2006.08218v52020
Deep metric learning using Triplet network
Elad Hoffer, Nir Ailon
cs.LGcs.CVstat.MLarXiv:1412.6622v42014
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Pin-Yu Chen, Huan Zhang, Yash Sharma +2
stat.MLcs.CRcs.LGarXiv:1708.03999v22017
Dream to Control: Learning Behaviors by Latent Imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba +1
cs.LGcs.AIcs.ROarXiv:1912.01603v32019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Erico Tjoa, Cuntai Guan
cs.LGcs.AIarXiv:1907.07374v52019
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier, Willem Waegeman
cs.LGstat.MLarXiv:1910.09457v32019
GENEB: Why Genomic Models Are Hard to Compare
Daria Ledneva, Mikhail Nuridinov, Denis Kuznetsov
cs.CLcs.LGq-bio.GNarXiv:2606.04525v42026
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Mandar Joshi, Danqi Chen, Yinhan Liu +3
cs.CLcs.LGarXiv:1907.10529v32019
Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference
Abhishek Divekar
cs.LGcs.AIcs.CLarXiv:2606.05308v12026
Mixed membership stochastic blockmodels
Edoardo M Airoldi, David M Blei, Stephen E Fienberg +1
stat.MEcs.LGmath.STarXiv:0705.4485v12007
Tabular Data: Deep Learning is Not All You Need
Ravid Shwartz-Ziv, Amitai Armon
cs.LGarXiv:2106.03253v22021
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Xiujun Li, Xi Yin, Chunyuan Li +9
cs.CVcs.CLcs.IRarXiv:2004.06165v52020
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez +5
cs.NEcs.LGarXiv:1606.04474v22016
LLM Explainability with Counterfactual Chains and Causal Graphs
Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor +1
cs.LGarXiv:2606.05972v12026
A Closer Look at Memorization in Deep Networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas +8
stat.MLcs.LGarXiv:1706.05394v22017
Benchmarking Single Image Dehazing and Beyond
Boyi Li, Wenqi Ren, Dengpan Fu +4
cs.CVcs.AIcs.LGarXiv:1712.04143v42017
DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models
Zhuoming Liu, Jinhong Lin, Kwan Man Cheng +3
cs.CVcs.AIcs.LGarXiv:2606.05758v12026
European Union regulations on algorithmic decision-making and a "right to explanation"
Bryce Goodman, Seth Flaxman
stat.MLcs.CYcs.LGarXiv:1606.08813v32016
In-Context Multiple Instance Learning
Alexander Möllers, Marvin Sextro, Julius Hense +2
cs.LGcs.AIcs.CVarXiv:2606.06458v12026
Video Swin Transformer
Ze Liu, Jia Ning, Yue Cao +4
cs.CVcs.AIcs.LGarXiv:2106.13230v12021
Towards VQA Models That Can Read
Amanpreet Singh, Vivek Natarajan, Meet Shah +5
cs.CLcs.CVcs.LGarXiv:1904.08920v22019
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage +1
cs.LGarXiv:1610.02527v12016
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle +1
cs.LGcs.CVstat.MLarXiv:1512.09300v22015
Learning Dexterous In-Hand Manipulation
OpenAI, Marcin Andrychowicz, Bowen Baker +14
cs.LGcs.AIcs.ROarXiv:1808.00177v52018
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Sergey Levine, Peter Pastor, Alex Krizhevsky +1
cs.LGcs.AIcs.CVarXiv:1603.02199v42016
Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper +8
cs.CLcs.LGcs.NEarXiv:1412.5567v22014
AsyncWebRL: Efficient Asynchronous Reinforcement Learning for Multi-Step Visual Web Agents
Hao Bai, Rui Yang, Chenlu Ye +3
cs.LGarXiv:2606.05597v32026
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu +6
cs.CLcs.LGstat.MLarXiv:2006.16668v12020

Machine Learning

Improving Factuality and Reasoning in Language Models through Multiagent Debate

Hyperparameters and Tuning Strategies for Random Forest

Inherent Trade-Offs in the Fair Determination of Risk Scores

When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models

Physics-informed neural networks (PINNs) for fluid mechanics: A review

Sequence Transduction with Recurrent Neural Networks

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Qwen-Image-Flash: Beyond Objective Design

SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction

Hypergraph Neural Networks

Self-critical Sequence Training for Image Captioning

STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

Voyager: An Open-Ended Embodied Agent with Large Language Models

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Off-Policy Deep Reinforcement Learning without Exploration

Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory

Modeling Tabular data using Conditional GAN

Reconciling modern machine learning practice and the bias-variance trade-off

Make-A-Video: Text-to-Video Generation without Text-Video Data

Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

GLU Variants Improve Transformer

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Stateful Visual Encoders for Vision-Language Models

Adversarial Examples Are Not Bugs, They Are Features

Representation Learning on Graphs: Methods and Applications

Federated Multi-Task Learning

Measuring Model Robustness via Fisher Information: Spectral Bounds, Theoretical Guarantees, and Practical Algorithms

TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration

Weight Uncertainty in Neural Networks

Designing Network Design Spaces

Self-supervised Learning: Generative or Contrastive

Deep metric learning using Triplet network

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Dream to Control: Learning Behaviors by Latent Imagination

A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods

GENEB: Why Genomic Models Are Hard to Compare

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference

Mixed membership stochastic blockmodels

Tabular Data: Deep Learning is Not All You Need

Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

Learning to learn by gradient descent by gradient descent

LLM Explainability with Counterfactual Chains and Causal Graphs

A Closer Look at Memorization in Deep Networks

Benchmarking Single Image Dehazing and Beyond

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models

European Union regulations on algorithmic decision-making and a "right to explanation"

In-Context Multiple Instance Learning

Video Swin Transformer

Towards VQA Models That Can Read

Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Autoencoding beyond pixels using a learned similarity metric

Learning Dexterous In-Hand Manipulation

Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection

Deep Speech: Scaling up end-to-end speech recognition

AsyncWebRL: Efficient Asynchronous Reinforcement Learning for Multi-Step Visual Web Agents

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding