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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6,301 to 6,360 of 6,792

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
Holographic Embeddings of Knowledge Graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
cs.AIcs.LGstat.MLarXiv:1510.04935v22015
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Jialin Wang +4
cs.IRcs.LGstat.MLarXiv:1803.03467v42018
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Huaxiu Yao, Fei Wu, Jintao Ke +6
cs.LGstat.MLarXiv:1802.08714v22018
Black Box Variational Inference
Rajesh Ranganath, Sean Gerrish, David M. Blei
stat.MLcs.LGstat.COarXiv:1401.0118v12013
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Jason Weston, Antoine Bordes, Sumit Chopra +4
cs.AIcs.CLstat.MLarXiv:1502.05698v102015
Techniques for Interpretable Machine Learning
Mengnan Du, Ninghao Liu, Xia Hu
cs.LGcs.AIstat.MLarXiv:1808.00033v32018
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand +3
cs.AIcs.LGstat.MLarXiv:1607.00148v22016
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Akara Supratak, Hao Dong, Chao Wu +1
stat.MLarXiv:1703.04046v22017
Deep Neural Networks as Gaussian Processes
Jaehoon Lee, Yasaman Bahri, Roman Novak +3
stat.MLcs.LGarXiv:1711.00165v32017
Pyro: Deep Universal Probabilistic Programming
Eli Bingham, Jonathan P. Chen, Martin Jankowiak +7
cs.LGcs.PLstat.MLarXiv:1810.09538v12018
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li +2
cs.LGcs.AIcs.CVarXiv:1811.03804v42018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
Pouya Samangouei, Maya Kabkab, Rama Chellappa
cs.CVcs.LGstat.MLarXiv:1805.06605v22018
The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt +7
cs.LGcs.MAstat.MLarXiv:1902.04043v52019
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
stat.MLcs.CRcs.LGarXiv:1805.07984v42018
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
cs.LGcs.NEstat.MLarXiv:1909.00590v52019
When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang +1
cs.LGcs.AIstat.MLarXiv:1906.08253v32019
What is the State of Neural Network Pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle +1
cs.LGstat.MLarXiv:2003.03033v12020
An Introduction to Conditional Random Fields
Charles Sutton, Andrew McCallum
stat.MLarXiv:1011.4088v12010
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi +4
cs.LGcs.CRcs.CVarXiv:1804.00792v22018
High Fidelity Neural Audio Compression
Alexandre Défossez, Jade Copet, Gabriel Synnaeve +1
eess.AScs.AIcs.SDarXiv:2210.13438v12022
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1
cs.LGcs.AIcs.CVarXiv:1604.06057v22016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu, Dilin Wang
stat.MLcs.LGarXiv:1608.04471v32016
Square Attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion +1
cs.LGcs.CRcs.CVarXiv:1912.00049v32019
Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi
cs.LGcs.CVstat.MLarXiv:1806.08342v12018
struc2vec: Learning Node Representations from Structural Identity
Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo
cs.SIcs.LGstat.MLarXiv:1704.03165v32017
Matrix Completion from a Few Entries
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
cs.LGstat.MLarXiv:0901.3150v42009
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Xiaolei Ma, Zhuang Dai, Zhengbing He +3
cs.LGstat.MLarXiv:1701.04245v42017
Overcoming catastrophic forgetting with hard attention to the task
Joan Serrà, Dídac Surís, Marius Miron +1
cs.LGcs.AIcs.NEarXiv:1801.01423v32018
OptNet: Differentiable Optimization as a Layer in Neural Networks
Brandon Amos, J. Zico Kolter
cs.LGcs.AImath.OCarXiv:1703.00443v52017
Mastering Atari with Discrete World Models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi +1
cs.LGcs.AIstat.MLarXiv:2010.02193v42020
Metrics for Multi-Class Classification: an Overview
Margherita Grandini, Enrico Bagli, Giorgio Visani
stat.MLcs.LGarXiv:2008.05756v12020
LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi +1
cs.LGstat.MLarXiv:1709.05206v12017
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Aravind Srinivas, Michael Laskin, Pieter Abbeel
cs.LGcs.CVstat.MLarXiv:2004.04136v42020
Deep Gaussian Processes
Andreas C. Damianou, Neil D. Lawrence
stat.MLcs.LGmath.PRarXiv:1211.0358v22012
Mastering Diverse Domains through World Models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba +1
cs.AIcs.LGstat.MLarXiv:2301.04104v22023
Graph U-Nets
Hongyang Gao, Shuiwang Ji
cs.LGstat.MLarXiv:1905.05178v12019
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, George Tucker +1
cs.LGstat.MLarXiv:1906.00949v22019
Learning Sparse Neural Networks through $L_0$ Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
stat.MLcs.LGarXiv:1712.01312v22017
Adversarially Learned Inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole +4
stat.MLcs.LGarXiv:1606.00704v32016
Self-Attention Graph Pooling
Junhyun Lee, Inyeop Lee, Jaewoo Kang
cs.LGstat.MLarXiv:1904.08082v42019
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer, Mitchell Stern
cs.LGcs.AIstat.MLarXiv:1804.04235v12018
Deep Interest Evolution Network for Click-Through Rate Prediction
Guorui Zhou, Na Mou, Ying Fan +5
stat.MLcs.IRcs.LGarXiv:1809.03672v52018
Few-Shot Learning with Graph Neural Networks
Victor Garcia, Joan Bruna
stat.MLcs.LGarXiv:1711.04043v32017
code2vec: Learning Distributed Representations of Code
Uri Alon, Meital Zilberstein, Omer Levy +1
cs.LGcs.AIcs.PLarXiv:1803.09473v52018
Distribution-Free Predictive Inference For Regression
Jing Lei, Max G'Sell, Alessandro Rinaldo +2
stat.MEmath.STstat.MLarXiv:1604.04173v22016
Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
cs.LGstat.MLarXiv:1509.00519v42015
Analyzing Federated Learning through an Adversarial Lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal +1
cs.LGcs.AIcs.CRarXiv:1811.12470v42018
Contrastive Representation Distillation
Yonglong Tian, Dilip Krishnan, Phillip Isola
cs.LGcs.CVstat.MLarXiv:1910.10699v32019
Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar
stat.MLcs.IRcs.LGarXiv:1207.6083v42012
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Robert Krajewski, Julian Bock, Laurent Kloeker +1
cs.CVcs.AIcs.IRarXiv:1810.05642v12018
Towards Accurate Generative Models of Video: A New Metric & Challenges
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach +3
cs.CVcs.AIcs.LGarXiv:1812.01717v22018
Gaussian Processes for Big Data
James Hensman, Nicolo Fusi, Neil D. Lawrence
cs.LGstat.MLarXiv:1309.6835v12013
Recurrent World Models Facilitate Policy Evolution
David Ha, Jürgen Schmidhuber
cs.LGstat.MLarXiv:1809.01999v12018
Flower: A Friendly Federated Learning Research Framework
Daniel J. Beutel, Taner Topal, Akhil Mathur +8
cs.LGcs.CVstat.MLarXiv:2007.14390v52020
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
Weijie Su, Stephen Boyd, Emmanuel J. Candes
stat.MLmath.CAmath.OCarXiv:1503.01243v22015

Machine Learning (stat)

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

Holographic Embeddings of Knowledge Graphs

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Black Box Variational Inference

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

Techniques for Interpretable Machine Learning

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

Deep Neural Networks as Gaussian Processes

Pyro: Deep Universal Probabilistic Programming

Gradient Descent Finds Global Minima of Deep Neural Networks

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

The StarCraft Multi-Agent Challenge

Adversarial Attacks on Neural Networks for Graph Data

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

When to Trust Your Model: Model-Based Policy Optimization

What is the State of Neural Network Pruning?

An Introduction to Conditional Random Fields

Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

High Fidelity Neural Audio Compression

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Square Attack: a query-efficient black-box adversarial attack via random search

Quantizing deep convolutional networks for efficient inference: A whitepaper

struc2vec: Learning Node Representations from Structural Identity

Matrix Completion from a Few Entries

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Overcoming catastrophic forgetting with hard attention to the task

OptNet: Differentiable Optimization as a Layer in Neural Networks

Mastering Atari with Discrete World Models

Metrics for Multi-Class Classification: an Overview

LSTM Fully Convolutional Networks for Time Series Classification

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Deep Gaussian Processes

Mastering Diverse Domains through World Models

Graph U-Nets

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Learning Sparse Neural Networks through $L_0$ Regularization

Adversarially Learned Inference

Self-Attention Graph Pooling

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Deep Interest Evolution Network for Click-Through Rate Prediction

Few-Shot Learning with Graph Neural Networks

code2vec: Learning Distributed Representations of Code

Distribution-Free Predictive Inference For Regression

Importance Weighted Autoencoders

Analyzing Federated Learning through an Adversarial Lens

Contrastive Representation Distillation

Determinantal point processes for machine learning

The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Towards Accurate Generative Models of Video: A New Metric & Challenges

Gaussian Processes for Big Data

Recurrent World Models Facilitate Policy Evolution

Flower: A Friendly Federated Learning Research Framework

A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights