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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6,001 to 6,060 of 6,790

SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi, David Poole
stat.MLcs.LGarXiv:1802.04868v22018
Noise2Self: Blind Denoising by Self-Supervision
Joshua Batson, Loic Royer
cs.CVcs.LGstat.MLarXiv:1901.11365v22019
Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang +2
stat.MLcs.LGarXiv:1802.04687v22018
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Aditya Golatkar, Alessandro Achille, Stefano Soatto
cs.LGstat.MLarXiv:1911.04933v52019
Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras +2
cs.LGcs.NEstat.MLarXiv:1804.11285v22018
Deep Image Prior
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
cs.CVstat.MLarXiv:1711.10925v42017
Maxout Networks
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza +2
stat.MLcs.LGarXiv:1302.4389v42013
Summaries:한국어
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen +1
cs.LGcs.AIcs.CGarXiv:2104.13478v22021
A Neural Representation of Sketch Drawings
David Ha, Douglas Eck
cs.NEcs.LGstat.MLarXiv:1704.03477v42017
Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, Ofir Nachum
cs.LGcs.AIstat.MLarXiv:1911.11361v12019
On a Formal Model of Safe and Scalable Self-driving Cars
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
cs.ROcs.AIstat.MLarXiv:1708.06374v62017
Adaptive Graph Convolutional Neural Networks
Ruoyu Li, Sheng Wang, Feiyun Zhu +1
cs.LGstat.MLarXiv:1801.03226v12018
Can Cascades be Predicted?
Justin Cheng, Lada A. Adamic, P. Alex Dow +2
cs.SIphysics.soc-phstat.MLarXiv:1403.4608v12014
Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Chris J. Maddison +6
cs.LGstat.MLarXiv:1807.01613v12018
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte +1
cs.LGstat.MLarXiv:1905.08883v32019
Neural Style Transfer: A Review
Yongcheng Jing, Yezhou Yang, Zunlei Feng +3
cs.CVcs.NEeess.IVarXiv:1705.04058v72017
Personalized Cross-Silo Federated Learning on Non-IID Data
Yutao Huang, Lingyang Chu, Zirui Zhou +4
cs.LGcs.DCstat.MLarXiv:2007.03797v52020
Deep Learning for Classical Japanese Literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto +3
cs.CVcs.LGstat.MLarXiv:1812.01718v12018
Soft-DTW: a Differentiable Loss Function for Time-Series
Marco Cuturi, Mathieu Blondel
stat.MLarXiv:1703.01541v22017
When Gaussian Process Meets Big Data: A Review of Scalable GPs
Haitao Liu, Yew-Soon Ong, Xiaobo Shen +1
stat.MLcs.LGarXiv:1807.01065v22018
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
Pengzhen Ren, Yun Xiao, Xiaojun Chang +4
cs.LGstat.MLarXiv:2006.02903v32020
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)
Padraig Cunningham, Sarah Jane Delany
cs.LGstat.MLarXiv:2004.04523v22020
The effect of data encoding on the expressive power of variational quantum machine learning models
Maria Schuld, Ryan Sweke, Johannes Jakob Meyer
quant-phstat.MLarXiv:2008.08605v22020
Topic Modeling in Embedding Spaces
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
cs.IRcs.CLcs.LGarXiv:1907.04907v12019
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
Xue Bin Peng, Aviral Kumar, Grace Zhang +1
cs.LGstat.MLarXiv:1910.00177v32019
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
cs.LGstat.MLarXiv:1909.01315v22019
A Survey on Distributed Machine Learning
Joost Verbraeken, Matthijs Wolting, Jonathan Katzy +3
cs.LGcs.DCstat.MLarXiv:1912.09789v12019
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini +2
cs.LGcs.CVstat.MLarXiv:2004.05718v52020
PDE-Net: Learning PDEs from Data
Zichao Long, Yiping Lu, Xianzhong Ma +1
math.NAcs.LGcs.NEarXiv:1710.09668v22017
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto +6
cs.LGstat.MLarXiv:1611.01838v52016
How to train your MAML
Antreas Antoniou, Harrison Edwards, Amos Storkey
cs.LGstat.MLarXiv:1810.09502v32018
Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
Zai Yang, Lihua Xie, Cishen Zhang
stat.APcs.ITstat.MLarXiv:1108.5838v42011
CauScale: Neural Causal Discovery at Scale
Bo Peng, Sirui Chen, Jiaguo Tian +2
cs.LGcs.AIstat.MLarXiv:2602.08629v22026
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, Daniel Soudry
stat.MLcs.LGarXiv:1705.08741v22017
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Sergey Levine
cs.LGcs.AIcs.ROarXiv:1805.00909v32018
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden, Sebastian Mayer, Katharina Beckh +11
stat.MLcs.AIcs.LGarXiv:1903.12394v32019
On the Expressive Power of Deep Neural Networks
Maithra Raghu, Ben Poole, Jon Kleinberg +2
stat.MLcs.AIcs.LGarXiv:1606.05336v62016
The State of Sparsity in Deep Neural Networks
Trevor Gale, Erich Elsen, Sara Hooker
cs.LGstat.MLarXiv:1902.09574v12019
Do Deep Generative Models Know What They Don't Know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +2
stat.MLcs.LGarXiv:1810.09136v32018
Diffusion Improves Graph Learning
Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann
cs.SIcs.AIcs.LGarXiv:1911.05485v62019
Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, Jan Kautz
stat.MLcs.LGarXiv:2106.05931v32021
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
cs.LGstat.MLarXiv:1807.00263v12018
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian +4
cs.LGcs.CRcs.SIarXiv:1806.02371v12018
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo +2
stat.MLcs.LGarXiv:1806.04910v22018
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang, Mark Harman, Lei Ma +1
cs.LGcs.AIcs.SEarXiv:1906.10742v22019
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Grégoire Montavon, Sebastian Bach, Alexander Binder +2
cs.LGstat.MLarXiv:1512.02479v12015
Gated Feedback Recurrent Neural Networks
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho +1
cs.NEcs.LGstat.MLarXiv:1502.02367v42015
Sparsified SGD with Memory
Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
cs.LGcs.DCcs.DSarXiv:1809.07599v22018
Bayesian Online Changepoint Detection
Ryan Prescott Adams, David J. C. MacKay
stat.MLarXiv:0710.3742v12007
Variational Dropout Sparsifies Deep Neural Networks
Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
stat.MLcs.LGarXiv:1701.05369v32017
TuckER: Tensor Factorization for Knowledge Graph Completion
Ivana Balažević, Carl Allen, Timothy M. Hospedales
cs.LGstat.MLarXiv:1901.09590v22019
A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans +1
cs.LGcs.AIstat.MLarXiv:1806.10758v32018
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Müller, Katharina Eggensperger +1
cs.LGstat.MLarXiv:2207.01848v62022
Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave +2
stat.MLcs.AIcs.CRarXiv:1704.08847v22017
A Comprehensive Survey of Deep Learning for Image Captioning
Md. Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin +1
cs.CVcs.LGstat.MLarXiv:1810.04020v22018
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang +2
cs.CVcs.AIcs.LGarXiv:1607.03516v22016
Deep learning to represent sub-grid processes in climate models
Stephan Rasp, Michael S. Pritchard, Pierre Gentine
physics.ao-phcs.LGstat.MLarXiv:1806.04731v32018
Task-Driven Dictionary Learning
Julien Mairal, Francis Bach, Jean Ponce
stat.MLarXiv:1009.5358v22010
Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
cs.AIcs.LGstat.MLarXiv:2007.00523v22020
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Rie Johnson, Tong Zhang
cs.CLcs.LGstat.MLarXiv:1412.1058v22014

Machine Learning (stat)

SimplE Embedding for Link Prediction in Knowledge Graphs

Noise2Self: Blind Denoising by Self-Supervision

Neural Relational Inference for Interacting Systems

Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks

Adversarially Robust Generalization Requires More Data

Deep Image Prior

Maxout Networks

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

A Neural Representation of Sketch Drawings

Behavior Regularized Offline Reinforcement Learning

On a Formal Model of Safe and Scalable Self-driving Cars

Adaptive Graph Convolutional Neural Networks

Can Cascades be Predicted?

Conditional Neural Processes

Explainable Machine Learning for Scientific Insights and Discoveries

Neural Style Transfer: A Review

Personalized Cross-Silo Federated Learning on Non-IID Data

Deep Learning for Classical Japanese Literature

Soft-DTW: a Differentiable Loss Function for Time-Series

When Gaussian Process Meets Big Data: A Review of Scalable GPs

A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

The effect of data encoding on the expressive power of variational quantum machine learning models

Topic Modeling in Embedding Spaces

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

A Survey on Distributed Machine Learning

Principal Neighbourhood Aggregation for Graph Nets

PDE-Net: Learning PDEs from Data

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys

How to train your MAML

Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference

CauScale: Neural Causal Discovery at Scale

Train longer, generalize better: closing the generalization gap in large batch training of neural networks

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review

Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems

On the Expressive Power of Deep Neural Networks

The State of Sparsity in Deep Neural Networks

Do Deep Generative Models Know What They Don't Know?

Diffusion Improves Graph Learning

Score-based Generative Modeling in Latent Space

Accurate Uncertainties for Deep Learning Using Calibrated Regression

Adversarial Attack on Graph Structured Data

Bilevel Programming for Hyperparameter Optimization and Meta-Learning

Machine Learning Testing: Survey, Landscapes and Horizons

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

Gated Feedback Recurrent Neural Networks

Sparsified SGD with Memory

Bayesian Online Changepoint Detection

Variational Dropout Sparsifies Deep Neural Networks

TuckER: Tensor Factorization for Knowledge Graph Completion

A Benchmark for Interpretability Methods in Deep Neural Networks

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Parseval Networks: Improving Robustness to Adversarial Examples

A Comprehensive Survey of Deep Learning for Image Captioning

Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

Deep learning to represent sub-grid processes in climate models

Task-Driven Dictionary Learning

Drug discovery with explainable artificial intelligence

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks