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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16,681 to 16,740 of 20,454

Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection
Quy-Anh Dang, Chris Ngo
cs.LGcs.AIarXiv:2601.19375v12026
ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation
Zihao Huang, Jundong Zhou, Xingwei Qu +2
cs.LGarXiv:2601.21420v12026
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
stat.MLcs.LGmath.OCarXiv:1703.00102v22017
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review
Jiaxin Li, Danfeng Hong, Lianru Gao +4
cs.CVcs.LGeess.SParXiv:2205.01380v12022
Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data
Grzegorz Stefanski, Alberto Presta, Michal Byra
cs.AIcs.CVcs.LGarXiv:2601.22141v12026
Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting
Krishna Bhatia, Harsh, Shalini Devendrababu
quant-phcs.LGarXiv:2608.23119v12026
Clipping-Free Policy Optimization for Large Language Models
Ömer Veysel Çağatan, Barış Akgün, Gözde Gül Şahin +1
cs.LGarXiv:2601.22801v12026
How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning
Liyan Xu, Mo Yu, Fandong Meng +1
cs.LGcs.CLarXiv:2602.02103v22026
Self-Rewarding Sequential Monte Carlo for Masked Diffusion Language Models
Ziwei Luo, Ziqi Jin, Lei Wang +2
cs.LGarXiv:2602.01849v12026
DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers
Ionut-Vlad Modoranu, Philip Zmushko, Erik Schultheis +2
cs.LGarXiv:2602.02016v22026
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Kexin Yi, Jiajun Wu, Chuang Gan +3
cs.AIcs.CLcs.CVarXiv:1810.02338v22018
ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning
Jingwei Song, Meng Chen, Jie Xiao +15
cs.LGcs.DCarXiv:2602.02192v52026
Reliable and Responsible Foundation Models: A Comprehensive Survey
Xinyu Yang, Junlin Han, Rishi Bommasani +49
cs.LGcs.AIcs.CLarXiv:2602.08145v12026
Agent-Omit: Adaptive Context Omission for Efficient LLM Agents
Yansong Ning, Jun Fang, Naiqiang Tan +1
cs.AIcs.LGarXiv:2602.04284v22026
Making Expert Reasoning Learnable with Self-Distillation
Ethan Mendes, Jungsoo Park, Alan Ritter
cs.LGcs.AIarXiv:2602.02405v22026
"I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time
Jinkun Chen, Fengxiang Cheng, Sijia Han +1
cs.AIcs.LGarXiv:2602.02863v12026
Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection
Dongwon Jo, Beomseok Kang, Jiwon Song +1
cs.CLcs.LGarXiv:2602.03216v32026
Training Data Efficiency in Multimodal Process Reward Models
Jinyuan Li, Chengsong Huang, Langlin Huang +4
cs.LGcs.CLcs.MMarXiv:2602.04145v22026
ChatGPT for Robotics: Design Principles and Model Abilities
Sai Vemprala, Rogerio Bonatti, Arthur Bucker +1
cs.AIcs.CLcs.HCarXiv:2306.17582v22023
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
cs.LGcs.AIcs.CVarXiv:1412.6614v42014
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Sebastian Raschka, Joshua Patterson, Corey Nolet
cs.LGstat.MLarXiv:2002.04803v22020
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Joel Z. Leibo, Vinicius Zambaldi, Marc Lanctot +2
cs.MAcs.AIcs.GTarXiv:1702.03037v12017
SplitLite: Low-Rank Residual Compression for Split Learning
Tao Li, Yulin Tang, Qi Guo +1
cs.LGcs.AIarXiv:2608.23018v12026
Efficiently Scaling Transformer Inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery +7
cs.LGcs.CLarXiv:2211.05102v12022
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
Haohan Wang, Xindi Wu, Zeyi Huang +1
cs.CVcs.LGarXiv:1905.13545v32019
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
stat.MLcs.LGarXiv:1406.2541v12014
Optimal Distributed Online Prediction using Mini-Batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir +1
cs.LGcs.DCmath.OCarXiv:1012.1367v22010
BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
Junyu Chen, Jungang Li, Jing Xiong +11
cs.LGarXiv:2602.04163v22026
Temporal Pair Consistency for Variance-Reduced Flow Matching
Chika Maduabuchi, Jindong Wang
cs.LGcs.AIcs.CVarXiv:2602.04908v22026
No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
Dmitry Karpov
cs.CLcs.AIcs.LGarXiv:2602.04442v12026
SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models
Quentin Guimard, Federico Bartsch, Simone Caldarella +3
cs.CVcs.AIcs.LGarXiv:2603.19028v12026
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
cs.LGstat.MLarXiv:2007.13518v42020
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Renrui Zhang, Dongzhi Jiang, Yichi Zhang +8
cs.CVcs.AIcs.CLarXiv:2403.14624v22024
Late-to-Early Training: LET LLMs Learn Earlier, So Faster and Better
Ji Zhao, Yufei Gu, Shitong Shao +3
cs.CLcs.LGarXiv:2602.05393v12026
VAE with a VampPrior
Jakub M. Tomczak, Max Welling
cs.LGcs.AIstat.MLarXiv:1705.07120v52017
Precision-Aware Variable Bit Processing Elements for Hardware-Efficient Systolic Array Designs
Dantu Nandini Devi, Madhav Rao
cs.ARcs.ETcs.LGarXiv:2608.22378v12026
Social Attention: Modeling Attention in Human Crowds
Anirudh Vemula, Katharina Muelling, Jean Oh
cs.ROcs.LGarXiv:1710.04689v22017
HashNet: Deep Learning to Hash by Continuation
Zhangjie Cao, Mingsheng Long, Jianmin Wang +1
cs.LGcs.CVarXiv:1702.00758v42017
Editing Factual Knowledge in Language Models
Nicola De Cao, Wilker Aziz, Ivan Titov
cs.CLcs.AIcs.LGarXiv:2104.08164v22021
Dreaming in Code for Curriculum Learning in Open-Ended Worlds
Konstantinos Mitsides, Maxence Faldor, Antoine Cully
cs.LGcs.AIcs.CLarXiv:2602.08194v12026
Label-Efficient Semantic Segmentation with Diffusion Models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov +2
cs.CVcs.LGarXiv:2112.03126v32021
FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models
Annemette Brok Pirchert, Jacob Nielsen, Mogens Henrik From +2
cs.LGarXiv:2602.08818v12026
On the Optimal Reasoning Length for RL-Trained Language Models
Daisuke Nohara, Taishi Nakamura, Rio Yokota
cs.CLcs.AIcs.LGarXiv:2602.09591v32026
Deep neural network models for computational histopathology: A survey
Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
eess.IVcs.CVcs.LGarXiv:1912.12378v22019
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Yisheng Song, Ting Wang, Subrota K Mondal +1
cs.LGarXiv:2205.06743v22022
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Maryam Fazel, Rong Ge, Sham M. Kakade +1
cs.LGstat.MLarXiv:1801.05039v32018
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton +1
cs.LGstat.MLarXiv:1912.01588v22019
Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models
Shiting Huang, Zecheng Li, Yu Zeng +7
cs.LGcs.AIarXiv:2602.10224v12026
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G. Northcutt, Anish Athalye, Jonas Mueller
stat.MLcs.AIcs.LGarXiv:2103.14749v42021
Deep Evidential Regression
Alexander Amini, Wilko Schwarting, Ava Soleimany +1
cs.LGcs.NEstat.MLarXiv:1910.02600v22019
How Architecture and Training Affect TPC Representations Across Experiments
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +11
cs.LGcs.CVnucl-exarXiv:2608.21756v12026
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver +1
cs.LGcs.AIstat.MLarXiv:1806.06923v12018
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
Mike Lambeta, Po-Wei Chou, Stephen Tian +9
cs.ROcs.LGeess.SYarXiv:2005.14679v12020
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart +9
cs.LGcs.AIcs.CVarXiv:1709.07857v22017
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
Iasonas Kokkinos
cs.CVcs.AIcs.LGarXiv:1609.02132v12016
Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5
Dongrui Liu, Yi Yu, Jie Zhang +18
cs.AIcs.CLcs.CVarXiv:2602.14457v12026
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo +7
cs.LGeess.IVstat.MLarXiv:1904.07773v62019
OPBench: A Graph Benchmark to Combat the Opioid Crisis
Tianyi Ma, Yiyang Li, Yiyue Qian +4
cs.LGcs.AIarXiv:2602.14602v12026
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Edward Choi, Siddharth Biswal, Bradley Malin +3
cs.LGcs.NEarXiv:1703.06490v32017
More accurate behavioral predictions with hybrid Bayesian-connectionist models
Brenden M. Lake, Akshay K. Jagadish, Guangyuan Jiang
cs.LGarXiv:2608.22154v12026

Machine Learning

Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection

ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data

Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting

Clipping-Free Policy Optimization for Large Language Models

How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning

Self-Rewarding Sequential Monte Carlo for Masked Diffusion Language Models

DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning

Reliable and Responsible Foundation Models: A Comprehensive Survey

Agent-Omit: Adaptive Context Omission for Efficient LLM Agents

Making Expert Reasoning Learnable with Self-Distillation

"I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time

Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection

Training Data Efficiency in Multimodal Process Reward Models

ChatGPT for Robotics: Design Principles and Model Abilities

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

SplitLite: Low-Rank Residual Compression for Split Learning

Efficiently Scaling Transformer Inference

High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

Optimal Distributed Online Prediction using Mini-Batches

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models

Temporal Pair Consistency for Variance-Reduced Flow Matching

No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data

SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models

FedML: A Research Library and Benchmark for Federated Machine Learning

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Late-to-Early Training: LET LLMs Learn Earlier, So Faster and Better

VAE with a VampPrior

Precision-Aware Variable Bit Processing Elements for Hardware-Efficient Systolic Array Designs

Social Attention: Modeling Attention in Human Crowds

HashNet: Deep Learning to Hash by Continuation

Editing Factual Knowledge in Language Models

Dreaming in Code for Curriculum Learning in Open-Ended Worlds

Label-Efficient Semantic Segmentation with Diffusion Models

FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models

On the Optimal Reasoning Length for RL-Trained Language Models

Deep neural network models for computational histopathology: A survey

A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator

Leveraging Procedural Generation to Benchmark Reinforcement Learning

Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Deep Evidential Regression

How Architecture and Training Affect TPC Representations Across Experiments

Implicit Quantile Networks for Distributional Reinforcement Learning

DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation

OPBench: A Graph Benchmark to Combat the Opioid Crisis

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

More accurate behavioral predictions with hybrid Bayesian-connectionist models