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.

Search paper metadata (including unsummarized papers)

4,561 to 4,620 of 6,772

Regularization for Deep Learning: A Taxonomy
Jan Kukačka, Vladimir Golkov, Daniel Cremers
cs.LGcs.AIcs.CVarXiv:1710.10686v12017
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
stat.MLcs.LGarXiv:2002.10118v22020
Generalization and Representational Limits of Graph Neural Networks
Vikas K. Garg, Stefanie Jegelka, Tommi Jaakkola
cs.LGstat.MLarXiv:2002.06157v12020
Learning to Optimize: A Primer and A Benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen +4
math.OCcs.LGstat.MLarXiv:2103.12828v22021
Optimal Transport for structured data with application on graphs
Titouan Vayer, Laetitia Chapel, Rémi Flamary +2
stat.MLcs.LGarXiv:1805.09114v32018
Summaries:한국어
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
Sebastian Johann Wetzel
cond-mat.stat-mechcs.LGstat.MLarXiv:1703.02435v22017
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
Dongxian Wu, Yisen Wang, Shu-Tao Xia +2
cs.LGcs.CRcs.CVarXiv:2002.05990v12020
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
Jinchao Liu, Margarita Osadchy, Lorna Ashton +3
cs.LGstat.MLarXiv:1708.09022v12017
Summaries:한국어
Learning how to explain neural networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber +4
stat.MLcs.LGarXiv:1705.05598v22017
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Oron Anschel, Nir Baram, Nahum Shimkin
cs.AIcs.LGstat.MLarXiv:1611.01929v42016
Full-Gradient Representation for Neural Network Visualization
Suraj Srinivas, Francois Fleuret
cs.LGcs.CVstat.MLarXiv:1905.00780v42019
Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham M. Kakade, Karan Singh +1
cs.LGcs.AIstat.MLarXiv:1812.02690v22018
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch +3
cs.LGstat.MLarXiv:2002.02886v42020
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
cs.LGstat.APstat.MLarXiv:2410.08770v12024
An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading
Balint Antal, Andras Hajdu
cs.CVcs.AIstat.AParXiv:1410.8577v12014
Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin Liang
cs.LGmath.OCstat.MLarXiv:2010.07962v32020
Troubling Trends in Machine Learning Scholarship
Zachary C. Lipton, Jacob Steinhardt
stat.MLcs.AIcs.LGarXiv:1807.03341v22018
Learning to Learn from Noisy Labeled Data
Junnan Li, Yongkang Wong, Qi Zhao +1
cs.LGcs.CVstat.MLarXiv:1812.05214v22018
Deep and Confident Prediction for Time Series at Uber
Lingxue Zhu, Nikolay Laptev
stat.MLarXiv:1709.01907v12017
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman, Danijar Hafner, George Tucker +2
cs.LGcs.AIstat.MLarXiv:1807.01675v22018
Inferring transportation modes from GPS trajectories using a convolutional neural network
Sina Dabiri, Kevin Heaslip
cs.LGstat.MLarXiv:1804.02386v12018
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei, Chenjun Xiao, Csaba Szepesvari +1
cs.LGstat.MLarXiv:2005.06392v32020
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
Guoyin Li, Ting Kei Pong
math.OCstat.MLarXiv:1602.02915v62016
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
Görkem Algan, Ilkay Ulusoy
cs.LGcs.CVstat.MLarXiv:1912.05170v32019
Hierarchical Generation of Molecular Graphs using Structural Motifs
Wengong Jin, Regina Barzilay, Tommi Jaakkola
cs.LGstat.MLarXiv:2002.03230v22020
A Kernel Test of Goodness of Fit
Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
stat.MLarXiv:1602.02964v42016
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer +1
cs.LGcs.AIcs.MAarXiv:2006.07869v42020
Optimal algorithms for smooth and strongly convex distributed optimization in networks
Kevin Scaman, Francis Bach, Sébastien Bubeck +2
math.OCstat.MLarXiv:1702.08704v22017
Tensor Decompositions for temporal knowledge base completion
Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier
stat.MLcs.LGarXiv:2004.04926v12020
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Tapio Schneider, Shiwei Lan, Andrew Stuart +1
stat.MLphysics.ao-pharXiv:1709.00037v32017
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Isabelle Augenstein, Mrinal Das, Sebastian Riedel +2
cs.CLcs.AIstat.MLarXiv:1704.02853v32017
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan +4
cs.IRcs.AIcs.LGarXiv:1901.09888v12019
Agnostic Estimation of Mean and Covariance
Kevin A. Lai, Anup B. Rao, Santosh Vempala
cs.DScs.LGstat.MLarXiv:1604.06968v22016
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Hongyu Ren, Weihua Hu, Jure Leskovec
cs.LGcs.CLstat.MLarXiv:2002.05969v22020
Evaluating the Search Phase of Neural Architecture Search
Kaicheng Yu, Christian Sciuto, Martin Jaggi +2
cs.LGstat.MLarXiv:1902.08142v32019
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
cs.LGcs.PFstat.MLarXiv:2001.09249v12020
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango +2
cs.LGstat.MLarXiv:2112.10510v72021
Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan, J. Zico Kolter
cs.LGcs.AImath.OCarXiv:1706.04156v32017
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde +6
cs.LGcs.AIstat.MLarXiv:2305.02997v42023
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Wei-Ning Hsu, Yu Zhang, James Glass
cs.LGcs.CLcs.SDarXiv:1709.07902v12017
Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist +2
cs.LGstat.MLarXiv:1711.07839v12017
Recurrent Neural Networks For Accurate RSSI Indoor Localization
Minh Tu Hoang, Brosnan Yuen, Xiaodai Dong +3
eess.SPcs.LGstat.MLarXiv:1903.11703v22019
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li +3
cs.LGcs.AIcs.CVarXiv:2009.11848v52020
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
cs.LGstat.MLarXiv:1808.04260v12018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
stat.MLcs.AIcs.LGarXiv:1706.00400v22017
The Risks of Invariant Risk Minimization
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
cs.LGcs.AIstat.MLarXiv:2010.05761v22020
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Tongwen Huang, Zhiqi Zhang, Junlin Zhang
cs.LGcs.AIstat.MLarXiv:1905.09433v12019
Neural networks for the prediction organic chemistry reactions
Jennifer N. Wei, David Duvenaud, Alán Aspuru-Guzik
physics.chem-phq-bio.QMstat.MLarXiv:1608.06296v22016
PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami +1
cs.GRcs.CVcs.LGarXiv:2002.10880v12020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal, Chongli Qin, Jonathan Uesato +2
stat.MLcs.AIcs.LGarXiv:2010.03593v32020
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
Péter Mernyei, Cătălina Cangea
cs.LGcs.SIstat.MLarXiv:2007.02901v22020
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Bei Peng, Tabish Rashid, Christian A. Schroeder de Witt +4
cs.LGcs.AIstat.MLarXiv:2003.06709v52020
Bayesian Nonparametric Hidden Semi-Markov Models
Matthew J. Johnson, Alan S. Willsky
stat.MEstat.APstat.MLarXiv:1203.1365v22012
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai +1
cs.LGcs.AIstat.MLarXiv:1906.04733v22019
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee
cs.LGstat.MLarXiv:2011.14878v22020
Variance-based regularization with convex objectives
John Duchi, Hongseok Namkoong
stat.MLmath.STarXiv:1610.02581v32016
Discrete Distribution Estimation under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage
stat.MLcs.LGarXiv:1602.07387v32016
CERT: Contrastive Self-supervised Learning for Language Understanding
Hongchao Fang, Sicheng Wang, Meng Zhou +2
cs.CLcs.LGstat.MLarXiv:2005.12766v22020
A Note on Over-Smoothing for Graph Neural Networks
Chen Cai, Yusu Wang
cs.LGstat.MLarXiv:2006.13318v12020
State Representation Learning for Control: An Overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou +1
cs.AIcs.LGstat.MLarXiv:1802.04181v22018

Machine Learning (stat)

Regularization for Deep Learning: A Taxonomy

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Generalization and Representational Limits of Graph Neural Networks

Learning to Optimize: A Primer and A Benchmark

Optimal Transport for structured data with application on graphs

Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders

Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution

Learning how to explain neural networks: PatternNet and PatternAttribution

Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

Full-Gradient Representation for Neural Network Visualization

Provably Efficient Maximum Entropy Exploration

Weakly-Supervised Disentanglement Without Compromises

Causal machine learning for predicting treatment outcomes

An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading

Bilevel Optimization: Convergence Analysis and Enhanced Design

Troubling Trends in Machine Learning Scholarship

Learning to Learn from Noisy Labeled Data

Deep and Confident Prediction for Time Series at Uber

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Inferring transportation modes from GPS trajectories using a convolutional neural network

On the Global Convergence Rates of Softmax Policy Gradient Methods

Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods

Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey

Hierarchical Generation of Molecular Graphs using Structural Motifs

A Kernel Test of Goodness of Fit

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Optimal algorithms for smooth and strongly convex distributed optimization in networks

Tensor Decompositions for temporal knowledge base completion

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

Agnostic Estimation of Mean and Covariance

Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

Evaluating the Search Phase of Neural Architecture Search

TiFL: A Tier-based Federated Learning System

Transformers Can Do Bayesian Inference

Gradient descent GAN optimization is locally stable

When Do Neural Nets Outperform Boosted Trees on Tabular Data?

Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

Application of generative autoencoder in de novo molecular design

Recurrent Neural Networks For Accurate RSSI Indoor Localization

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

iNNvestigate neural networks!

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

The Risks of Invariant Risk Minimization

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

Neural networks for the prediction organic chemistry reactions

PolyGen: An Autoregressive Generative Model of 3D Meshes

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

FACMAC: Factored Multi-Agent Centralised Policy Gradients

Bayesian Nonparametric Hidden Semi-Markov Models

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

Explaining by Removing: A Unified Framework for Model Explanation

Variance-based regularization with convex objectives

Discrete Distribution Estimation under Local Privacy

CERT: Contrastive Self-supervised Learning for Language Understanding

A Note on Over-Smoothing for Graph Neural Networks

State Representation Learning for Control: An Overview