Machine Learning (stat)

Papers filed under stat.ML 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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6,481 to 6,540 of 6,780

Deep Learning for Sentiment Analysis : A Survey
Lei Zhang, Shuai Wang, Bing Liu
cs.CLcs.IRcs.LGarXiv:1801.07883v22018
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg, Gabriel G. Erion, Su-In Lee
cs.LGstat.MLarXiv:1802.03888v32018
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase +1
cs.LGcs.DCstat.MLarXiv:1910.02054v32019
AutoAugment: Learning Augmentation Policies from Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane +2
cs.CVcs.LGstat.MLarXiv:1805.09501v32018
Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell +1
stat.MLcs.CYcs.LGarXiv:1703.06856v32017
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming
Saeed Ghadimi, Guanghui Lan
math.OCcs.CCstat.MLarXiv:1309.5549v12013
CatBoost: gradient boosting with categorical features support
Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
cs.LGcs.MSstat.MLarXiv:1810.11363v12018
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, Yann LeCun
cs.LGcs.CVstat.MLarXiv:1511.05440v62015
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
Natural Adversarial Examples
Dan Hendrycks, Kevin Zhao, Steven Basart +2
cs.LGcs.CVstat.MLarXiv:1907.07174v42019
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Shiyang Li, Xiaoyong Jin, Yao Xuan +4
cs.LGstat.MLarXiv:1907.00235v32019
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
cs.LGstat.MLarXiv:2007.02133v12020
Tutorial on Variational Autoencoders
Carl Doersch
stat.MLcs.LGarXiv:1606.05908v32016
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer +4
cs.LGcs.AIstat.MLarXiv:1811.04551v52018
Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz +3
cs.LGstat.MLarXiv:1606.04934v22016
KAN: Kolmogorov-Arnold Networks
Ziming Liu, Yixuan Wang, Sachin Vaidya +5
cs.LGcond-mat.dis-nncs.AIarXiv:2404.19756v52024
On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez
cs.CLcs.AIcs.LGarXiv:2606.00467v12026
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang +2
eess.AScs.CLcs.LGarXiv:2009.09761v32020
Deep Bayesian Active Learning with Image Data
Yarin Gal, Riashat Islam, Zoubin Ghahramani
cs.LGcs.CVstat.MLarXiv:1703.02910v12017
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +424
cs.CVcs.AIcs.LGarXiv:1811.02629v32018
Challenges in Representation Learning: A report on three machine learning contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier +25
stat.MLcs.LGarXiv:1307.0414v12013
Sharpness-Aware Minimization for Efficiently Improving Generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi +1
cs.LGstat.MLarXiv:2010.01412v32020
Alias-Free Generative Adversarial Networks
Tero Karras, Miika Aittala, Samuli Laine +4
cs.CVcs.AIcs.LGarXiv:2106.12423v42021
Summaries:한국어
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta +3
cs.LGcs.CVcs.ROarXiv:2001.06782v42020
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Lei Bai, Lina Yao, Can Li +2
cs.LGstat.MLarXiv:2007.02842v22020
Noise2Noise: Learning Image Restoration without Clean Data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren +4
cs.CVcs.LGstat.MLarXiv:1803.04189v32018
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
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
Memory Aware Synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny +2
cs.CVcs.AIstat.MLarXiv:1711.09601v42017
Hypergraph Neural Networks
Yifan Feng, Haoxuan You, Zizhao Zhang +2
cs.LGstat.MLarXiv:1809.09401v32018
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
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
Hierarchical structure and the prediction of missing links in networks
Aaron Clauset, Cristopher Moore, M. E. J. Newman
stat.MLphysics.soc-phq-bio.MNarXiv:0811.0484v12008
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
Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks
Rahul Dey, Fathi M. Salem
cs.NEstat.MLarXiv:1701.05923v12017
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras +3
stat.MLcs.CRcs.CVarXiv:1905.02175v42019
Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi +1
cs.LGstat.MLarXiv:1705.10467v22017
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu +1
stat.MLcs.LGarXiv:1505.05424v22015
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
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier, Willem Waegeman
cs.LGstat.MLarXiv:1910.09457v32019
Mixed membership stochastic blockmodels
Edoardo M Airoldi, David M Blei, Stephen E Fienberg +1
stat.MEcs.LGmath.STarXiv:0705.4485v12007
A Closer Look at Memorization in Deep Networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas +8
stat.MLcs.LGarXiv:1706.05394v22017
European Union regulations on algorithmic decision-making and a "right to explanation"
Bryce Goodman, Seth Flaxman
stat.MLcs.CYcs.LGarXiv:1606.08813v32016
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
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu +6
cs.CLcs.LGstat.MLarXiv:2006.16668v12020
Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan +3
cs.AIcs.LGstat.MLarXiv:1806.00069v32018
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren +6
stat.MLcs.LGarXiv:1906.02530v22019
Towards Principled Methods for Training Generative Adversarial Networks
Martin Arjovsky, Léon Bottou
stat.MLcs.LGarXiv:1701.04862v12017
KGAT: Knowledge Graph Attention Network for Recommendation
Xiang Wang, Xiangnan He, Yixin Cao +2
cs.LGcs.IRstat.MLarXiv:1905.07854v22019
DeepXDE: A deep learning library for solving differential equations
Lu Lu, Xuhui Meng, Zhiping Mao +1
cs.LGphysics.comp-phstat.MLarXiv:1907.04502v22019
Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
cs.LGcs.AIstat.MLarXiv:1605.05273v42016
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt +1
cs.CVcs.LGstat.MLarXiv:1902.10811v22019

Machine Learning (stat)

Deep Learning for Sentiment Analysis : A Survey

Consistent Individualized Feature Attribution for Tree Ensembles

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

AutoAugment: Learning Augmentation Policies from Data

Counterfactual Fairness

Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming

CatBoost: gradient boosting with categorical features support

Deep multi-scale video prediction beyond mean square error

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

Natural Adversarial Examples

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

Simple and Deep Graph Convolutional Networks

Tutorial on Variational Autoencoders

Learning Latent Dynamics for Planning from Pixels

Improving Variational Inference with Inverse Autoregressive Flow

KAN: Kolmogorov-Arnold Networks

On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

DiffWave: A Versatile Diffusion Model for Audio Synthesis

Deep Bayesian Active Learning with Image Data

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Challenges in Representation Learning: A report on three machine learning contests

Sharpness-Aware Minimization for Efficiently Improving Generalization

Alias-Free Generative Adversarial Networks

Gradient Surgery for Multi-Task Learning

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

Noise2Noise: Learning Image Restoration without Clean Data

Hyperparameters and Tuning Strategies for Random Forest

Inherent Trade-Offs in the Fair Determination of Risk Scores

Sequence Transduction with Recurrent Neural Networks

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

Memory Aware Synapses: Learning what (not) to forget

Hypergraph Neural Networks

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Off-Policy Deep Reinforcement Learning without Exploration

Modeling Tabular data using Conditional GAN

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

Hierarchical structure and the prediction of missing links in networks

GLU Variants Improve Transformer

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

Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks

Adversarial Examples Are Not Bugs, They Are Features

Federated Multi-Task Learning

Weight Uncertainty in Neural Networks

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

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

Mixed membership stochastic blockmodels

A Closer Look at Memorization in Deep Networks

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

Autoencoding beyond pixels using a learned similarity metric

Learning Dexterous In-Hand Manipulation

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Explaining Explanations: An Overview of Interpretability of Machine Learning

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Towards Principled Methods for Training Generative Adversarial Networks

KGAT: Knowledge Graph Attention Network for Recommendation

DeepXDE: A deep learning library for solving differential equations

Learning Convolutional Neural Networks for Graphs

Do ImageNet Classifiers Generalize to ImageNet?