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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6,361 to 6,420 of 6,790

Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, J. Zico Kolter
cs.LGstat.MLarXiv:2001.03994v12020
Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy, Hubert Banville, Isabela Albuquerque +3
cs.LGeess.SPstat.MLarXiv:1901.05498v22019
Graph Transformer Networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim +2
cs.LGcs.SIstat.MLarXiv:1911.06455v22019
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas, Logan Engstrom, Anish Athalye +1
cs.CVcs.CRstat.MLarXiv:1804.08598v32018
Horovod: fast and easy distributed deep learning in TensorFlow
Alexander Sergeev, Mike Del Balso
cs.LGstat.MLarXiv:1802.05799v32018
Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun +2
cs.LGstat.MLarXiv:2002.06440v12020
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello +2
stat.MLcs.LGarXiv:2004.07211v22020
On the Number of Linear Regions of Deep Neural Networks
Guido Montúfar, Razvan Pascanu, Kyunghyun Cho +1
stat.MLcs.LGcs.NEarXiv:1402.1869v22014
Semi-Supervised Learning with Ladder Networks
Antti Rasmus, Harri Valpola, Mikko Honkala +2
cs.NEcs.LGstat.MLarXiv:1507.02672v22015
Deep Learning for Anomaly Detection: A Review
Guansong Pang, Chunhua Shen, Longbing Cao +1
cs.LGcs.CVstat.MLarXiv:2007.02500v32020
Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
physics.chem-phstat.MLarXiv:2010.07067v22020
Challenges of Big Data Analysis
Jianqing Fan, Fang Han, Han Liu
stat.MLarXiv:1308.1479v22013
Flood Prediction Using Machine Learning Models: Literature Review
Amir Mosavi, Pinar Ozturk, Kwok-wing Chau
cs.LGstat.MLarXiv:1908.02781v12019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan
cs.LGcs.CYstat.MLarXiv:1905.07697v22019
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Deli Chen, Yankai Lin, Wei Li +3
cs.LGcs.SIstat.MLarXiv:1909.03211v22019
Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, Jure Leskovec
cs.LGq-bio.MNstat.MLarXiv:1802.00543v22018
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon, James Jordon, Mihaela van der Schaar
cs.LGstat.MLarXiv:1806.02920v12018
Deep clustering: Discriminative embeddings for segmentation and separation
John R. Hershey, Zhuo Chen, Jonathan Le Roux +1
cs.NEcs.LGstat.MLarXiv:1508.04306v12015
Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi +5
cs.LGcs.CVstat.MLarXiv:1803.00676v12018
Improved Knowledge Distillation via Teacher Assistant
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li +3
cs.LGcs.AIstat.MLarXiv:1902.03393v22019
Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang +4
cs.LGstat.MLarXiv:2007.04612v32020
Ditto: Fair and Robust Federated Learning Through Personalization
Tian Li, Shengyuan Hu, Ahmad Beirami +1
cs.LGstat.MLarXiv:2012.04221v32020
Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu, Richard Zhang, Deepak Pathak +4
cs.CVcs.GRstat.MLarXiv:1711.11586v42017
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu
cs.LGq-fin.CPstat.MLarXiv:1911.13288v12019
Do Vision Transformers See Like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith +2
cs.CVcs.AIcs.LGarXiv:2108.08810v22021
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Benjamin Recht, Yoram Singer
cs.LGmath.OCstat.MLarXiv:1509.01240v22015
Adversarial Training for Free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi +6
cs.LGcs.CRcs.CVarXiv:1904.12843v22019
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda +3
stat.MLphysics.chem-pharXiv:1706.08566v52017
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Han Cai, Ligeng Zhu, Song Han
cs.LGcs.CVstat.MLarXiv:1812.00332v22018
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu, Suyog Gupta
stat.MLcs.LGarXiv:1710.01878v22017
Improving Transferability of Adversarial Examples with Input Diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou +4
cs.CVcs.LGstat.MLarXiv:1803.06978v42018
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep Ravikumar +1
stat.MLcs.AIcs.LGarXiv:1803.01422v22018
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Qinbin Li, Zeyi Wen, Zhaomin Wu +5
cs.LGcs.CRcs.DBarXiv:1907.09693v72019
Improved Techniques for Training Score-Based Generative Models
Yang Song, Stefano Ermon
cs.LGcs.CVstat.MLarXiv:2006.09011v22020
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
Benjamin Shickel, Patrick Tighe, Azra Bihorac +1
cs.LGstat.MLarXiv:1706.03446v22017
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
Wojciech Samek, Thomas Wiegand, Klaus-Robert Müller
cs.AIcs.CYcs.NEarXiv:1708.08296v12017
Solving Rubik's Cube with a Robot Hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz +16
cs.LGcs.AIcs.CVarXiv:1910.07113v12019
In Search of Lost Domain Generalization
Ishaan Gulrajani, David Lopez-Paz
cs.LGstat.MLarXiv:2007.01434v12020
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Ce Zhang, Huan Zhang +3
math.OCcs.DCcs.LGarXiv:1705.09056v52017
Meta-Learning with Latent Embedding Optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski +4
cs.LGcs.CVstat.MLarXiv:1807.05960v32018
Geom-GCN: Geometric Graph Convolutional Networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang +2
cs.LGcs.CVstat.MLarXiv:2002.05287v22020
Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman +1
stat.MLcs.IRcs.LGarXiv:1802.05814v12018
A Survey of Deep Active Learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang +5
cs.LGstat.MLarXiv:2009.00236v22020
DeepDTA: Deep Drug-Target Binding Affinity Prediction
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
stat.MLcs.LGarXiv:1801.10193v22018
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann Dauphin, Razvan Pascanu, Caglar Gulcehre +3
cs.LGmath.OCstat.MLarXiv:1406.2572v12014
Deep Exploration via Bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel +1
cs.LGcs.AIeess.SYarXiv:1602.04621v32016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi, Julie Nutini, Mark Schmidt
cs.LGmath.OCstat.COarXiv:1608.04636v42016
Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le
cs.CVcs.LGstat.MLarXiv:1805.08974v32018
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He +7
cs.LGcs.CLstat.MLarXiv:2002.04745v22020
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Wieland Brendel, Jonas Rauber, Matthias Bethge
stat.MLcs.CRcs.CVarXiv:1712.04248v22017
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt +3
cs.LGcs.MAstat.MLarXiv:2003.08839v22020
Deep & Cross Network for Ad Click Predictions
Ruoxi Wang, Bin Fu, Gang Fu +1
cs.LGstat.MLarXiv:1708.05123v12017
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen +6
cs.LGcs.SIstat.MLarXiv:1902.10191v32019
Rényi Divergence and Kullback-Leibler Divergence
Tim van Erven, Peter Harremoës
cs.ITmath.STstat.MLarXiv:1206.2459v22012
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua, Roberto Calandra, Rowan McAllister +1
cs.LGcs.AIcs.ROarXiv:1805.12114v22018
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Chaofan Tao +3
cs.LGcs.AIcs.CVarXiv:1806.10574v52018
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Yao Qin, Dongjin Song, Haifeng Chen +3
cs.LGstat.MLarXiv:1704.02971v42017
Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff +3
cs.LGphysics.comp-phstat.MLarXiv:2002.09405v22020
Large-Margin Softmax Loss for Convolutional Neural Networks
Weiyang Liu, Yandong Wen, Zhiding Yu +1
stat.MLcs.LGarXiv:1612.02295v42016
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Wei-Lin Chiang, Xuanqing Liu, Si Si +3
cs.LGcs.AIstat.MLarXiv:1905.07953v22019

Machine Learning (stat)

Fast is better than free: Revisiting adversarial training

Deep learning-based electroencephalography analysis: a systematic review

Graph Transformer Networks

Black-box Adversarial Attacks with Limited Queries and Information

Horovod: fast and easy distributed deep learning in TensorFlow

Federated Learning with Matched Averaging

Dark Experience for General Continual Learning: a Strong, Simple Baseline

On the Number of Linear Regions of Deep Neural Networks

Semi-Supervised Learning with Ladder Networks

Deep Learning for Anomaly Detection: A Review

Machine Learning Force Fields

Challenges of Big Data Analysis

Flood Prediction Using Machine Learning Models: Literature Review

Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View

Modeling polypharmacy side effects with graph convolutional networks

GAIN: Missing Data Imputation using Generative Adversarial Nets

Deep clustering: Discriminative embeddings for segmentation and separation

Meta-Learning for Semi-Supervised Few-Shot Classification

Improved Knowledge Distillation via Teacher Assistant

Concept Bottleneck Models

Ditto: Fair and Robust Federated Learning Through Personalization

Toward Multimodal Image-to-Image Translation

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Do Vision Transformers See Like Convolutional Neural Networks?

Train faster, generalize better: Stability of stochastic gradient descent

Adversarial Training for Free!

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

To prune, or not to prune: exploring the efficacy of pruning for model compression

Improving Transferability of Adversarial Examples with Input Diversity

DAGs with NO TEARS: Continuous Optimization for Structure Learning

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Improved Techniques for Training Score-Based Generative Models

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Solving Rubik's Cube with a Robot Hand

In Search of Lost Domain Generalization

Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

Meta-Learning with Latent Embedding Optimization

Geom-GCN: Geometric Graph Convolutional Networks

Variational Autoencoders for Collaborative Filtering

A Survey of Deep Active Learning

DeepDTA: Deep Drug-Target Binding Affinity Prediction

Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

Deep Exploration via Bootstrapped DQN

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition

Do Better ImageNet Models Transfer Better?

On Layer Normalization in the Transformer Architecture

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Deep & Cross Network for Ad Click Predictions

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Rényi Divergence and Kullback-Leibler Divergence

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

This Looks Like That: Deep Learning for Interpretable Image Recognition

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

Learning to Simulate Complex Physics with Graph Networks

Large-Margin Softmax Loss for Convolutional Neural Networks

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks