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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6,241 to 6,300 of 6,786

A Hybrid Approach to Privacy-Preserving Federated Learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar +4
cs.LGstat.MLarXiv:1812.03224v22018
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Jie Gui, Zhenan Sun, Yonggang Wen +2
cs.LGstat.MLarXiv:2001.06937v12020
Bayesian Active Learning for Classification and Preference Learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani +1
stat.MLcs.LGarXiv:1112.5745v12011
Deep Forest
Zhi-Hua Zhou, Ji Feng
cs.LGstat.MLarXiv:1702.08835v42017
Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
cs.LGstat.MLarXiv:1902.00146v12019
Tune: A Research Platform for Distributed Model Selection and Training
Richard Liaw, Eric Liang, Robert Nishihara +3
cs.LGcs.DCstat.MLarXiv:1807.05118v12018
TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause +3
cs.LGcs.NEstat.MLarXiv:2007.08663v12020
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
Jiyang Gao, Chen Sun, Hang Zhao +4
cs.CVcs.LGstat.MLarXiv:2005.04259v12020
Directional Message Passing for Molecular Graphs
Johannes Gasteiger, Janek Groß, Stephan Günnemann
cs.LGphysics.comp-phstat.MLarXiv:2003.03123v22020
Don't Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying +1
cs.LGcs.CVcs.DCarXiv:1711.00489v22017
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky +6
stat.MLarXiv:1502.05700v22015
Supervised Random Walks: Predicting and Recommending Links in Social Networks
L. Backstrom, J. Leskovec
cs.SIcs.AIcs.DSarXiv:1011.4071v12010
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong +5
cs.LGcs.SIstat.MLarXiv:2006.09963v32020
Meta Networks
Tsendsuren Munkhdalai, Hong Yu
cs.LGstat.MLarXiv:1703.00837v22017
Scaling Vision with Sparse Mixture of Experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa +5
cs.CVcs.LGstat.MLarXiv:2106.05974v12021
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor +5
cs.LGcs.SIstat.MLarXiv:1905.00067v32019
Towards Robust Interpretability with Self-Explaining Neural Networks
David Alvarez-Melis, Tommi S. Jaakkola
cs.LGstat.MLarXiv:1806.07538v22018
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca +3
cs.LGstat.MLarXiv:2006.10637v32020
ChemCrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Oliver Schilter +3
physics.chem-phstat.MLarXiv:2304.05376v52023
A Network-based End-to-End Trainable Task-oriented Dialogue System
Tsung-Hsien Wen, David Vandyke, Nikola Mrksic +5
cs.CLcs.AIcs.NEarXiv:1604.04562v32016
Inferring Networks of Diffusion and Influence
Manuel Gomez-Rodriguez, Jure Leskovec, Andreas Krause
cs.DScs.SIphysics.soc-pharXiv:1006.0234v32010
WaveGlow: A Flow-based Generative Network for Speech Synthesis
Ryan Prenger, Rafael Valle, Bryan Catanzaro
cs.SDcs.AIcs.LGarXiv:1811.00002v12018
Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato, Andrew M. Dai, Ian Goodfellow
stat.MLcs.LGarXiv:1605.07725v42016
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson +2
stat.MLcs.CRcs.LGarXiv:1610.05755v42016
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
Asif Iqbal Khan, Junaid Latief Shah, Mudasir Bhat
eess.IVcs.LGstat.MLarXiv:2004.04931v32020
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Sifan Wang, Hanwen Wang, Paris Perdikaris
cs.LGmath.NAstat.MLarXiv:2103.10974v12021
Knowledge Graph Convolutional Networks for Recommender Systems
Hongwei Wang, Miao Zhao, Xing Xie +2
cs.IRcs.LGstat.MLarXiv:1904.12575v12019
Reinforcement Learning for Solving the Vehicle Routing Problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder +1
cs.AIcs.LGstat.MLarXiv:1802.04240v22018
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng +3
cs.LGstat.MLarXiv:2009.03509v52020
Deep Double Descent: Where Bigger Models and More Data Hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal +3
cs.LGcs.CVcs.NEarXiv:1912.02292v12019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Dylan Slack, Sophie Hilgard, Emily Jia +2
cs.LGcs.AIstat.MLarXiv:1911.02508v22019
Data Augmentation Generative Adversarial Networks
Antreas Antoniou, Amos Storkey, Harrison Edwards
stat.MLcs.CVcs.LGarXiv:1711.04340v32017
Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly +1
stat.MLcs.LGarXiv:1711.01558v42017
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Han Zhang, Tao Xu, Hongsheng Li +4
cs.CVcs.AIstat.MLarXiv:1710.10916v32017
Composition-based Multi-Relational Graph Convolutional Networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin +1
cs.LGstat.MLarXiv:1911.03082v22019
Symmetric Cross Entropy for Robust Learning with Noisy Labels
Yisen Wang, Xingjun Ma, Zaiyi Chen +3
cs.LGcs.CVstat.MLarXiv:1908.06112v12019
Scaling Laws for Reward Model Overoptimization
Leo Gao, John Schulman, Jacob Hilton
cs.LGstat.MLarXiv:2210.10760v12022
How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling +3
stat.MLcs.LGarXiv:0912.1128v12009
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
Yan Duan, John Schulman, Xi Chen +3
cs.AIcs.LGcs.NEarXiv:1611.02779v22016
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Chung-Cheng Chiu, Tara N. Sainath, Yonghui Wu +11
cs.CLcs.SDeess.ASarXiv:1712.01769v62017
Deep Learning: A Critical Appraisal
Gary Marcus
cs.AIcs.LGstat.MLarXiv:1801.00631v12018
Importance Estimation for Neural Network Pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree +2
cs.LGcs.CVstat.MLarXiv:1906.10771v12019
Relief-Based Feature Selection: Introduction and Review
Ryan J. Urbanowicz, Melissa Meeker, William LaCava +2
cs.DScs.LGstat.MLarXiv:1711.08421v22017
DKN: Deep Knowledge-Aware Network for News Recommendation
Hongwei Wang, Fuzheng Zhang, Xing Xie +1
stat.MLcs.LGarXiv:1801.08284v22018
Captum: A unified and generic model interpretability library for PyTorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin +8
cs.LGcs.AIstat.MLarXiv:2009.07896v12020
Manopt, a Matlab toolbox for optimization on manifolds
Nicolas Boumal, Bamdev Mishra, P. -A. Absil +1
cs.MScs.LGmath.OCarXiv:1308.5200v12013
Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner +5
cs.LGcs.CVstat.MLarXiv:2006.15055v22020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang, Xuhui Meng, George Em Karniadakis
stat.MLcs.LGarXiv:2003.06097v12020
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
Leslie N. Smith
cs.LGcs.CVcs.NEarXiv:1803.09820v22018
FedMD: Heterogenous Federated Learning via Model Distillation
Daliang Li, Junpu Wang
cs.LGstat.MLarXiv:1910.03581v12019
BEGAN: Boundary Equilibrium Generative Adversarial Networks
David Berthelot, Thomas Schumm, Luke Metz
cs.LGstat.MLarXiv:1703.10717v42017
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen, Harri Valpola
cs.NEcs.LGstat.MLarXiv:1703.01780v62017
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow +2
cs.SEcs.CRcs.LGarXiv:1909.03496v12019
A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto, Shixiang Shane Gu
cs.LGcs.AIstat.MLarXiv:2106.06860v22021
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, Matthias Bethge
stat.MLcs.LGarXiv:1511.01844v32015
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen +5
cs.LGcs.AIcs.NEarXiv:2003.00688v52020
A Review of Feature Selection Methods Based on Mutual Information
Jorge R. Vergara, Pablo A. Estévez
cs.LGstat.MLarXiv:1509.07577v12015
Virtual Worlds as Proxy for Multi-Object Tracking Analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon +1
cs.CVcs.LGcs.NEarXiv:1605.06457v12016
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu +3
cs.LGstat.MLarXiv:2003.00982v52020
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder +3
cs.AIcs.CVcs.LGarXiv:1902.10178v12019

Machine Learning (stat)

A Hybrid Approach to Privacy-Preserving Federated Learning

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Bayesian Active Learning for Classification and Preference Learning

Deep Forest

Agnostic Federated Learning

Tune: A Research Platform for Distributed Model Selection and Training

TUDataset: A collection of benchmark datasets for learning with graphs

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

Directional Message Passing for Molecular Graphs

Don't Decay the Learning Rate, Increase the Batch Size

Scalable Bayesian Optimization Using Deep Neural Networks

Supervised Random Walks: Predicting and Recommending Links in Social Networks

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Meta Networks

Scaling Vision with Sparse Mixture of Experts

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

Towards Robust Interpretability with Self-Explaining Neural Networks

Temporal Graph Networks for Deep Learning on Dynamic Graphs

ChemCrow: Augmenting large-language models with chemistry tools

A Network-based End-to-End Trainable Task-oriented Dialogue System

Inferring Networks of Diffusion and Influence

WaveGlow: A Flow-based Generative Network for Speech Synthesis

Adversarial Training Methods for Semi-Supervised Text Classification

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets

Knowledge Graph Convolutional Networks for Recommender Systems

Reinforcement Learning for Solving the Vehicle Routing Problem

Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

Deep Double Descent: Where Bigger Models and More Data Hurt

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

Data Augmentation Generative Adversarial Networks

Wasserstein Auto-Encoders

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Composition-based Multi-Relational Graph Convolutional Networks

Symmetric Cross Entropy for Robust Learning with Noisy Labels

Scaling Laws for Reward Model Overoptimization

How to Explain Individual Classification Decisions

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

Deep Learning: A Critical Appraisal

Importance Estimation for Neural Network Pruning

Relief-Based Feature Selection: Introduction and Review

DKN: Deep Knowledge-Aware Network for News Recommendation

Captum: A unified and generic model interpretability library for PyTorch

Manopt, a Matlab toolbox for optimization on manifolds

Object-Centric Learning with Slot Attention

B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay

FedMD: Heterogenous Federated Learning via Model Distillation

BEGAN: Boundary Equilibrium Generative Adversarial Networks

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

A Minimalist Approach to Offline Reinforcement Learning

A note on the evaluation of generative models

Out-of-Distribution Generalization via Risk Extrapolation (REx)

A Review of Feature Selection Methods Based on Mutual Information

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

Benchmarking Graph Neural Networks

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn