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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18,601 to 18,660 of 20,219

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
cs.CVcs.LGstat.MLarXiv:1803.01229v12018
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Weijie Su, Xizhou Zhu, Yue Cao +4
cs.CVcs.CLcs.LGarXiv:1908.08530v42019
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont +6
cs.CVcs.CLcs.LGarXiv:2111.02114v12021
Efficient Lifelong Learning with A-GEM
Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach +1
cs.LGstat.MLarXiv:1812.00420v22018
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
Elad Ben-Zaken, Shauli Ravfogel, Yoav Goldberg
cs.LGcs.CLarXiv:2106.10199v52021
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
cs.LGcs.AIstat.MLarXiv:1811.12359v42018
Learning Implicit Fields for Generative Shape Modeling
Zhiqin Chen, Hao Zhang
cs.GRcs.CVcs.LGarXiv:1812.02822v52018
Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks
Ali Narin, Ceren Kaya, Ziynet Pamuk
eess.IVcs.CVcs.LGarXiv:2003.10849v32020
Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, Pavel Laskov
cs.LGcs.CRstat.MLarXiv:1206.6389v32012
Analyzing Learned Molecular Representations for Property Prediction
Kevin Yang, Kyle Swanson, Wengong Jin +12
cs.LGstat.MLarXiv:1904.01561v52019
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes +4
cs.LGstat.MLarXiv:1905.12265v32019
Fast Inference from Transformers via Speculative Decoding
Yaniv Leviathan, Matan Kalman, Yossi Matias
cs.LGcs.CLarXiv:2211.17192v22022
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang, Linfeng Zhang, Jiequn Han +1
physics.comp-phcs.LGphysics.chem-pharXiv:1712.03641v22017
On the Continuity of Rotation Representations in Neural Networks
Yi Zhou, Connelly Barnes, Jingwan Lu +2
cs.LGstat.MLarXiv:1812.07035v42018
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Justin Fu, Aviral Kumar, Ofir Nachum +2
cs.LGstat.MLarXiv:2004.07219v42020
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
Esteban Samaniego, Cosmin Anitescu, Somdatta Goswami +5
stat.MLcs.LGmath.AParXiv:1908.10407v22019
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Lasse Espeholt, Hubert Soyer, Remi Munos +9
cs.LGcs.AIarXiv:1802.01561v32018
A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Will Dabney, Rémi Munos
cs.LGcs.AIstat.MLarXiv:1707.06887v12017
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow
cs.CRcs.LGarXiv:1605.07277v12016
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
Weinan E, Bing Yu
cs.LGstat.MLarXiv:1710.00211v12017
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
Kyle Hundman, Valentino Constantinou, Christopher Laporte +2
cs.LGstat.MLarXiv:1802.04431v32018
Session-based Recommendation with Graph Neural Networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu +3
cs.IRcs.LGstat.MLarXiv:1811.00855v42018
Deep Learning for Sentiment Analysis : A Survey
Lei Zhang, Shuai Wang, Bing Liu
cs.CLcs.IRcs.LGarXiv:1801.07883v22018
How can embedding models bind concepts?
Arnas Uselis, Darina Koishigarina, Seong Joon Oh
cs.CVcs.LGarXiv:2605.31503v12026
Learning from Simulated and Unsupervised Images through Adversarial Training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel +3
cs.CVcs.LGcs.NEarXiv:1612.07828v22016
Language Models (Mostly) Know What They Know
Saurav Kadavath, Tom Conerly, Amanda Askell +33
cs.CLcs.AIcs.LGarXiv:2207.05221v42022
Highway Networks
Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber
cs.LGcs.NEarXiv:1505.00387v22015
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi +3
cs.CVcs.GRcs.LGarXiv:2008.02268v32020
StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement
Junwon Seo, Sushant Veer, Ran Tian +6
cs.CVcs.AIcs.LGarXiv:2606.00267v12026
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg, Gabriel G. Erion, Su-In Lee
cs.LGstat.MLarXiv:1802.03888v32018
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20
cs.LGarXiv:2012.07421v32020
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase +1
cs.LGcs.DCstat.MLarXiv:1910.02054v32019
Understanding Neural Networks Through Deep Visualization
Jason Yosinski, Jeff Clune, Anh Nguyen +2
cs.CVcs.LGcs.NEarXiv:1506.06579v12015
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Yue Cao, Jiarui Xu, Stephen Lin +2
cs.CVcs.AIcs.LGarXiv:1904.11492v12019
Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar +1
cs.LGarXiv:1705.10528v12017
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Gautier Izacard, Edouard Grave
cs.CLcs.LGarXiv:2007.01282v22020
Deep Recurrent Q-Learning for Partially Observable MDPs
Matthew Hausknecht, Peter Stone
cs.LGarXiv:1507.06527v42015
Calibrate Before Use: Improving Few-Shot Performance of Language Models
Tony Z. Zhao, Eric Wallace, Shi Feng +2
cs.CLcs.LGarXiv:2102.09690v22021
HyperNetworks
David Ha, Andrew Dai, Quoc V. Le
cs.LGarXiv:1609.09106v42016
Named Entity Recognition with Bidirectional LSTM-CNNs
Jason P. C. Chiu, Eric Nichols
cs.CLcs.LGcs.NEarXiv:1511.08308v52015
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Naman Jain, King Han, Alex Gu +7
cs.SEcs.CLcs.LGarXiv:2403.07974v22024
AutoAugment: Learning Augmentation Policies from Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane +2
cs.CVcs.LGstat.MLarXiv:1805.09501v32018
Segmenter: Transformer for Semantic Segmentation
Robin Strudel, Ricardo Garcia, Ivan Laptev +1
cs.CVcs.AIcs.LGarXiv:2105.05633v32021
Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell +1
stat.MLcs.CYcs.LGarXiv:1703.06856v32017
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas +1
cs.AIcs.LGcs.MAarXiv:1605.06676v22016
CatBoost: gradient boosting with categorical features support
Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
cs.LGcs.MSstat.MLarXiv:1810.11363v12018
Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu, Xinyun Chen, Chang Liu +1
cs.LGarXiv:1611.02770v32016
MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models
Chenglin Liu, Xun Wang, Ruishuo Chen +2
cs.LGcs.AIcs.CLarXiv:2608.18827v12026
GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis
Haochen Zhang, Gengwei Zhang, Laura Yao +2
cs.CLcs.LGarXiv:2608.13741v12026
Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani +7
cs.LGcs.AIarXiv:2606.10587v12026
TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
Yuliang Yan, Shuo Yan, Haochun Tang +2
cs.LGcs.AIarXiv:2607.22143v22026
MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators
Yushi Huang, Xiangxin Zhou, Jun Zhang +2
cs.CVcs.LGarXiv:2607.15273v22026
AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
Taicheng Guo, Nitesh V. Chawla, Olaf Wiest +1
cs.AIcs.CLcs.LGarXiv:2605.11518v22026
Rethinking Muon Beyond Pretraining: Spectral Failures and High-Pass Remedies for VLA and RLVR
Chongyu Fan, Gaowen Liu, Mingyi Hong +2
cs.LGarXiv:2605.19282v12026
Continual Harness: Online Adaptation for Self-Improving Foundation Agents
Seth Karten, Joel Zhang, Tersoo Upaa +5
cs.LGcs.AIarXiv:2605.09998v12026
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, Yann LeCun
cs.LGcs.CVstat.MLarXiv:1511.05440v62015
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Zhengxiao Du, Yujie Qian, Xiao Liu +4
cs.CLcs.AIcs.LGarXiv:2103.10360v22021
cuDNN: Efficient Primitives for Deep Learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch +4
cs.NEcs.LGcs.MSarXiv:1410.0759v32014
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini, David Wagner
cs.LGcs.CRcs.CVarXiv:1705.07263v22017
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio, Francis Bach, Simon Lacoste-Julien
cs.LGmath.OCstat.MLarXiv:1407.0202v32014

Machine Learning

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Efficient Lifelong Learning with A-GEM

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Learning Implicit Fields for Generative Shape Modeling

Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks

Poisoning Attacks against Support Vector Machines

Analyzing Learned Molecular Representations for Property Prediction

Strategies for Pre-training Graph Neural Networks

Fast Inference from Transformers via Speculative Decoding

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

On the Continuity of Rotation Representations in Neural Networks

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

A Distributional Perspective on Reinforcement Learning

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

Session-based Recommendation with Graph Neural Networks

Deep Learning for Sentiment Analysis : A Survey

How can embedding models bind concepts?

Learning from Simulated and Unsupervised Images through Adversarial Training

Language Models (Mostly) Know What They Know

Highway Networks

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement

Consistent Individualized Feature Attribution for Tree Ensembles

WILDS: A Benchmark of in-the-Wild Distribution Shifts

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Understanding Neural Networks Through Deep Visualization

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

Constrained Policy Optimization

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Deep Recurrent Q-Learning for Partially Observable MDPs

Calibrate Before Use: Improving Few-Shot Performance of Language Models

HyperNetworks

Named Entity Recognition with Bidirectional LSTM-CNNs

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

AutoAugment: Learning Augmentation Policies from Data

Segmenter: Transformer for Semantic Segmentation

Counterfactual Fairness

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

CatBoost: gradient boosting with categorical features support

Delving into Transferable Adversarial Examples and Black-box Attacks

MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models

GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis

Towards Diverse Scientific Hypothesis Search with Large Language Models

TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators

AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive

Rethinking Muon Beyond Pretraining: Spectral Failures and High-Pass Remedies for VLA and RLVR

Continual Harness: Online Adaptation for Self-Improving Foundation Agents

Deep multi-scale video prediction beyond mean square error

GLM: General Language Model Pretraining with Autoregressive Blank Infilling

cuDNN: Efficient Primitives for Deep Learning

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives