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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6,421 to 6,480 of 6,790

E(n) Equivariant Graph Neural Networks
Victor Garcia Satorras, Emiel Hoogeboom, Max Welling
cs.LGstat.MLarXiv:2102.09844v32021
Transformers in Time Series: A Survey
Qingsong Wen, Tian Zhou, Chaoli Zhang +4
cs.LGcs.AIeess.SParXiv:2202.07125v52022
The Ethics of AI Ethics -- An Evaluation of Guidelines
Thilo Hagendorff
cs.AIcs.CYcs.LGarXiv:1903.03425v22019
Molecular Graph Convolutions: Moving Beyond Fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl +2
stat.MLcs.LGarXiv:1603.00856v32016
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk +3
stat.MLcs.CVcs.LGarXiv:1912.02781v22019
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, William Buchwalter
cs.LGstat.MLarXiv:1906.00910v22019
Evidential Deep Learning to Quantify Classification Uncertainty
Murat Sensoy, Lance Kaplan, Melih Kandemir
cs.LGstat.MLarXiv:1806.01768v32018
Masked Autoregressive Flow for Density Estimation
George Papamakarios, Theo Pavlakou, Iain Murray
stat.MLcs.LGarXiv:1705.07057v42017
A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
cs.LGcs.DScs.NEarXiv:1811.03962v52018
Rethinking the Value of Network Pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou +2
cs.LGcs.CVstat.MLarXiv:1810.05270v22018
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Ian J. Goodfellow, Mehdi Mirza, Da Xiao +2
stat.MLcs.LGcs.NEarXiv:1312.6211v32013
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Yu Rong, Wenbing Huang, Tingyang Xu +1
cs.LGcs.NIstat.MLarXiv:1907.10903v42019
Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma, Tim Salimans, Max Welling
stat.MLcs.LGstat.COarXiv:1506.02557v22015
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown
cs.LGcs.CVstat.MLarXiv:1909.06335v12019
AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems
Nicole Koenigstein
cs.MAcs.AIcs.LGarXiv:2605.27466v12026
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, Tommi Jaakkola
cs.LGcs.NEstat.MLarXiv:1802.04364v42018
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Kaiqing Zhang, Zhuoran Yang, Tamer Başar
cs.LGcs.AIcs.MAarXiv:1911.10635v22019
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Yoav Goldberg, Omer Levy
cs.CLcs.LGstat.MLarXiv:1402.3722v12014
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini, Alessandro Rozza, Aditya Menon +2
stat.MLcs.LGarXiv:1609.03683v22016
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal, Zoubin Ghahramani
stat.MLarXiv:1512.05287v52015
Understanding intermediate layers using linear classifier probes
Guillaume Alain, Yoshua Bengio
stat.MLcs.LGarXiv:1610.01644v42016
A guide to convolution arithmetic for deep learning
Vincent Dumoulin, Francesco Visin
stat.MLcs.LGcs.NEarXiv:1603.07285v22016
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren, Wenyuan Zeng, Bin Yang +1
cs.LGstat.MLarXiv:1803.09050v32018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao +2
cs.CVcs.DCcs.LGarXiv:1712.01887v32017
Recursive Partitioning for Heterogeneous Causal Effects
Susan Athey, Guido Imbens
stat.MLecon.EMarXiv:1504.01132v32015
MINE: Mutual Information Neural Estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar +4
cs.LGstat.MLarXiv:1801.04062v52018
Differentially Private Federated Learning: A Client Level Perspective
Robin C. Geyer, Tassilo Klein, Moin Nabi
cs.CRcs.LGstat.MLarXiv:1712.07557v22017
Deep learning for universal linear embeddings of nonlinear dynamics
Bethany Lusch, J. Nathan Kutz, Steven L. Brunton
math.DScs.LGstat.MLarXiv:1712.09707v22017
Character-Aware Neural Language Models
Yoon Kim, Yacine Jernite, David Sontag +1
cs.CLcs.NEstat.MLarXiv:1508.06615v42015
A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp +1
stat.MLcs.LGarXiv:1503.00759v32015
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe Kiela
cs.AIcs.LGstat.MLarXiv:1705.08039v22017
Heterogeneous Graph Transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang +1
cs.LGcs.SIstat.MLarXiv:2003.01332v12020
Ray: A Distributed Framework for Emerging AI Applications
Philipp Moritz, Robert Nishihara, Stephanie Wang +8
cs.DCcs.AIcs.LGarXiv:1712.05889v22017
How Does Batch Normalization Help Optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas +1
stat.MLcs.LGcs.NEarXiv:1805.11604v52018
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor +8
cs.LGcs.AIcs.CVarXiv:1806.10293v32018
Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey +1
cs.LGcs.AIstat.MLarXiv:1810.12894v12018
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth Fong, Andrea Vedaldi
cs.CVcs.AIcs.LGarXiv:1704.03296v42017
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados +1
cs.LGstat.MLarXiv:1905.10437v42019
Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani, Amir Hosein Khasahmadi
cs.LGstat.MLarXiv:2006.05582v12020
TabNet: Attentive Interpretable Tabular Learning
Sercan O. Arik, Tomas Pfister
cs.LGstat.MLarXiv:1908.07442v52019
Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz +2
cs.LGcs.AIstat.MLarXiv:1811.11682v22018
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller +1
physics.chem-phcond-mat.dis-nncond-mat.mtrl-sciarXiv:1109.2618v12011
Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
cs.LGcs.CLcs.CVarXiv:1812.04606v32018
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu +5
cs.LGstat.MLarXiv:1812.01097v32018
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen +2
stat.MLcs.AIcs.LGarXiv:1703.03864v22017
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, Ryota Tomioka
stat.MLcs.LGstat.MEarXiv:1606.00709v12016
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski +1
cs.LGcs.SIstat.MLarXiv:1811.05868v22018
Supervised Topic Models
David M. Blei, Jon D. McAuliffe
stat.MLarXiv:1003.0783v12010
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Sébastien Bubeck, Nicolò Cesa-Bianchi
cs.LGstat.MLarXiv:1204.5721v22012
Diffusion Posterior Sampling for General Noisy Inverse Problems
Hyungjin Chung, Jeongsol Kim, Michael T. Mccann +2
stat.MLcs.AIcs.CVarXiv:2209.14687v42022
Multi-Task Learning as Multi-Objective Optimization
Ozan Sener, Vladlen Koltun
cs.LGstat.MLarXiv:1810.04650v22018
Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V. Le +2
cs.AIcs.LGstat.MLarXiv:1611.09940v32016
MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz +13
cs.LGcs.DCcs.MSarXiv:1505.06807v12015
Memory Networks
Jason Weston, Sumit Chopra, Antoine Bordes
cs.AIcs.CLstat.MLarXiv:1410.3916v112014
Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy, Sanjay Chawla
cs.LGstat.MLarXiv:1901.03407v22019
Root Mean Square Layer Normalization
Biao Zhang, Rico Sennrich
cs.LGcs.CLstat.MLarXiv:1910.07467v12019
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
Jared Katzman, Uri Shaham, Jonathan Bates +3
stat.MLcs.NEarXiv:1606.00931v32016
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
Efficient Lifelong Learning with A-GEM
Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach +1
cs.LGstat.MLarXiv:1812.00420v22018
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
cs.LGcs.AIstat.MLarXiv:1811.12359v42018

Machine Learning (stat)

E(n) Equivariant Graph Neural Networks

Transformers in Time Series: A Survey

The Ethics of AI Ethics -- An Evaluation of Guidelines

Molecular Graph Convolutions: Moving Beyond Fingerprints

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Learning Representations by Maximizing Mutual Information Across Views

Evidential Deep Learning to Quantify Classification Uncertainty

Masked Autoregressive Flow for Density Estimation

A Convergence Theory for Deep Learning via Over-Parameterization

Rethinking the Value of Network Pruning

An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Variational Dropout and the Local Reparameterization Trick

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems

Junction Tree Variational Autoencoder for Molecular Graph Generation

Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

Understanding intermediate layers using linear classifier probes

A guide to convolution arithmetic for deep learning

Learning to Reweight Examples for Robust Deep Learning

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Recursive Partitioning for Heterogeneous Causal Effects

MINE: Mutual Information Neural Estimation

Differentially Private Federated Learning: A Client Level Perspective

Deep learning for universal linear embeddings of nonlinear dynamics

Character-Aware Neural Language Models

A Review of Relational Machine Learning for Knowledge Graphs

Poincaré Embeddings for Learning Hierarchical Representations

Heterogeneous Graph Transformer

Ray: A Distributed Framework for Emerging AI Applications

How Does Batch Normalization Help Optimization?

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Exploration by Random Network Distillation

Interpretable Explanations of Black Boxes by Meaningful Perturbation

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Contrastive Multi-View Representation Learning on Graphs

TabNet: Attentive Interpretable Tabular Learning

Experience Replay for Continual Learning

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Deep Anomaly Detection with Outlier Exposure

LEAF: A Benchmark for Federated Settings

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Pitfalls of Graph Neural Network Evaluation

Supervised Topic Models

Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems

Diffusion Posterior Sampling for General Noisy Inverse Problems

Multi-Task Learning as Multi-Objective Optimization

Neural Combinatorial Optimization with Reinforcement Learning

MLlib: Machine Learning in Apache Spark

Memory Networks

Deep Learning for Anomaly Detection: A Survey

Root Mean Square Layer Normalization

DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

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

Efficient Lifelong Learning with A-GEM

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations