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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4,441 to 4,500 of 6,784

An Autoencoder Approach to Learning Bilingual Word Representations
Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle +4
cs.CLcs.LGstat.MLarXiv:1402.1454v12014
Accelerating Federated Learning via Momentum Gradient Descent
Wei Liu, Li Chen, Yunfei Chen +1
cs.LGstat.MLarXiv:1910.03197v22019
Learning Graph Embedding with Adversarial Training Methods
Shirui Pan, Ruiqi Hu, Sai-fu Fung +3
cs.LGstat.MLarXiv:1901.01250v22019
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain +2
cs.LGcs.DSstat.MLarXiv:1706.03175v12017
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman +1
stat.MLcs.LGarXiv:1802.08665v12018
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Samet Oymak, Mahdi Soltanolkotabi
cs.LGcs.ITmath.OCarXiv:1902.04674v12019
Unsupervised Attributed Multiplex Network Embedding
Chanyoung Park, Donghyun Kim, Jiawei Han +1
cs.LGstat.MLarXiv:1911.06750v22019
Concolic Testing for Deep Neural Networks
Youcheng Sun, Min Wu, Wenjie Ruan +3
cs.LGcs.SEstat.MLarXiv:1805.00089v22018
One Model To Learn Them All
Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer +4
cs.LGstat.MLarXiv:1706.05137v12017
Deep Generative Models in Engineering Design: A Review
Lyle Regenwetter, Amin Heyrani Nobari, Faez Ahmed
cs.LGstat.MLarXiv:2110.10863v42021
Training-image based geostatistical inversion using a spatial generative adversarial neural network
Eric Laloy, Romain Hérault, Diederik Jacques +1
stat.MLcs.CVphysics.geo-pharXiv:1708.04975v22017
Linear Algebraic Structure of Word Senses, with Applications to Polysemy
Sanjeev Arora, Yuanzhi Li, Yingyu Liang +2
cs.CLcs.LGstat.MLarXiv:1601.03764v62016
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal +2
stat.MLcs.AIstat.MEarXiv:1202.0515v42012
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
cs.LGeess.SYphysics.comp-pharXiv:1909.12077v52019
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon +1
cs.LGstat.MLarXiv:1306.3212v12013
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras +2
stat.MLcs.LGarXiv:1807.00882v12018
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro +1
cs.HCstat.MLarXiv:1704.01942v22017
How to Train Your Energy-Based Models
Yang Song, Diederik P. Kingma
cs.LGstat.MLarXiv:2101.03288v22021
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann
cs.LGcs.CRcs.SIarXiv:1809.01093v32018
A General Theory of Concave Regularization for High Dimensional Sparse Estimation Problems
Cun-Hui Zhang, Tong Zhang
stat.MLarXiv:1108.4988v22011
Rényi Differential Privacy of the Sampled Gaussian Mechanism
Ilya Mironov, Kunal Talwar, Li Zhang
cs.LGcs.CRstat.MLarXiv:1908.10530v12019
Learning Tree-based Deep Model for Recommender Systems
Han Zhu, Xiang Li, Pengye Zhang +4
stat.MLcs.IRcs.LGarXiv:1801.02294v52018
A Survey on Multi-View Clustering
Guoqing Chao, Shiliang Sun, Jinbo Bi
cs.LGstat.MLarXiv:1712.06246v22017
Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
stat.MLcs.AIcs.LGarXiv:1911.12247v22019
Truncated Power Method for Sparse Eigenvalue Problems
Xiao-Tong Yuan, Tong Zhang
stat.MLcs.AIarXiv:1112.2679v12011
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Qiuhua Huang, Renke Huang, Weituo Hao +3
cs.LGeess.SYstat.MLarXiv:1903.03712v22019
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi, Z. Zhang, G. E. Karniadakis
cs.NEcs.LGmath.NAarXiv:1912.00873v12019
Scikit-Multiflow: A Multi-output Streaming Framework
Jacob Montiel, Jesse Read, Albert Bifet +1
cs.LGstat.MLarXiv:1807.04662v12018
Learning protein sequence embeddings using information from structure
Tristan Bepler, Bonnie Berger
cs.LGq-bio.BMstat.MLarXiv:1902.08661v22019
L2 Regularization versus Batch and Weight Normalization
Twan van Laarhoven
cs.LGstat.MLarXiv:1706.05350v12017
Post-hoc Concept Bottleneck Models
Mert Yuksekgonul, Maggie Wang, James Zou
cs.LGcs.AIstat.MLarXiv:2205.15480v22022
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora, Nadav Cohen, Noah Golowich +1
cs.LGcs.NEstat.MLarXiv:1810.02281v32018
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
Meng Tang, Yimin Liu, Louis J. Durlofsky
cs.LGphysics.comp-phstat.MLarXiv:1908.05823v12019
Constrained Bayesian Optimization with Noisy Experiments
Benjamin Letham, Brian Karrer, Guilherme Ottoni +1
stat.MLcs.LGstat.AParXiv:1706.07094v22017
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
cs.LGstat.MLarXiv:1901.09504v32019
Robust Large Margin Deep Neural Networks
Jure Sokolic, Raja Giryes, Guillermo Sapiro +1
stat.MLcs.LGcs.NEarXiv:1605.08254v32016
Reinforcement Learning for Relation Classification from Noisy Data
Jun Feng, Minlie Huang, Li Zhao +2
cs.IRcs.LGstat.MLarXiv:1808.08013v12018
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noé
stat.MLcs.LGphysics.chem-pharXiv:2006.02425v22020
Regularizing Class-wise Predictions via Self-knowledge Distillation
Sukmin Yun, Jongjin Park, Kimin Lee +1
cs.LGcs.CVstat.MLarXiv:2003.13964v22020
Domain-Adversarial Neural Networks
Hana Ajakan, Pascal Germain, Hugo Larochelle +2
stat.MLcs.LGcs.NEarXiv:1412.4446v22014
Structured Pruning of Large Language Models
Ziheng Wang, Jeremy Wohlwend, Tao Lei
cs.CLcs.LGstat.MLarXiv:1910.04732v22019
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
Vladimir Feinberg, Alvin Wan, Ion Stoica +3
cs.LGcs.AIstat.MLarXiv:1803.00101v12018
Distributionally Robust Logistic Regression
Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn
math.OCstat.MLarXiv:1509.09259v32015
Neural Cognitive Diagnosis for Intelligent Education Systems
Fei Wang, Qi Liu, Enhong Chen +5
cs.LGcs.CYstat.MLarXiv:1908.08733v32019
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Brian L. Trippe, Jason Yim, Doug Tischer +4
q-bio.BMcs.LGstat.MLarXiv:2206.04119v22022
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
Jaideep Pathak, Alexander Wikner, Rebeckah Fussell +4
cs.LGnlin.CDstat.MLarXiv:1803.04779v12018
Adversarial Removal of Demographic Attributes from Text Data
Yanai Elazar, Yoav Goldberg
cs.CLcs.LGstat.MLarXiv:1808.06640v22018
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
Paulo R. de O. da Costa, Alp Akcay, Yingqian Zhang +1
cs.LGstat.MLarXiv:1907.07480v12019
Quant GANs: Deep Generation of Financial Time Series
Magnus Wiese, Robert Knobloch, Ralf Korn +1
q-fin.MFcs.LGq-fin.CParXiv:1907.06673v22019
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
cs.LGcs.CVstat.MLarXiv:1906.01620v32019
Generative Adversarial Active Learning for Unsupervised Outlier Detection
Yezheng Liu, Zhe Li, Chong Zhou +4
cs.LGstat.MLarXiv:1809.10816v42018
Parametric UMAP embeddings for representation and semi-supervised learning
Tim Sainburg, Leland McInnes, Timothy Q Gentner
cs.LGcs.CGq-bio.QMarXiv:2009.12981v42020
A New Approach to Probabilistic Programming Inference
Frank Wood, Jan Willem van de Meent, Vikash Mansinghka
stat.MLcs.AIcs.PLarXiv:1507.00996v22015
Supermasks in Superposition
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu +4
cs.LGcs.AIstat.MLarXiv:2006.14769v32020
Graph Information Bottleneck
Tailin Wu, Hongyu Ren, Pan Li +1
cs.LGstat.MLarXiv:2010.12811v12020
Summaries:한국어
SE(3) diffusion model with application to protein backbone generation
Jason Yim, Brian L. Trippe, Valentin De Bortoli +4
cs.LGq-bio.QMstat.MLarXiv:2302.02277v32023
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Le Wu, Junwei Li, Peijie Sun +3
cs.SIcs.IRcs.LGarXiv:2002.00844v42020
Latent ODEs for Irregularly-Sampled Time Series
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
cs.LGstat.MLarXiv:1907.03907v12019
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio +20
cs.CVcs.CRcs.LGarXiv:1804.00097v12018
Deep learning for molecular design - a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge +1
cs.LGphysics.chem-phstat.MLarXiv:1903.04388v32019

Machine Learning (stat)

An Autoencoder Approach to Learning Bilingual Word Representations

Accelerating Federated Learning via Momentum Gradient Descent

Learning Graph Embedding with Adversarial Training Methods

Recovery Guarantees for One-hidden-layer Neural Networks

Learning Latent Permutations with Gumbel-Sinkhorn Networks

Towards moderate overparameterization: global convergence guarantees for training shallow neural networks

Unsupervised Attributed Multiplex Network Embedding

Concolic Testing for Deep Neural Networks

One Model To Learn Them All

Deep Generative Models in Engineering Design: A Review

Training-image based geostatistical inversion using a spatial generative adversarial neural network

Linear Algebraic Structure of Word Senses, with Applications to Polysemy

High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

How to Train Your Energy-Based Models

Adversarial Attacks on Node Embeddings via Graph Poisoning

A General Theory of Concave Regularization for High Dimensional Sparse Estimation Problems

Rényi Differential Privacy of the Sampled Gaussian Mechanism

Learning Tree-based Deep Model for Recommender Systems

A Survey on Multi-View Clustering

Contrastive Learning of Structured World Models

Truncated Power Method for Sparse Eigenvalue Problems

Adaptive Power System Emergency Control using Deep Reinforcement Learning

Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Scikit-Multiflow: A Multi-output Streaming Framework

Learning protein sequence embeddings using information from structure

L2 Regularization versus Batch and Weight Normalization

Post-hoc Concept Bottleneck Models

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Constrained Bayesian Optimization with Noisy Experiments

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Robust Large Margin Deep Neural Networks

Reinforcement Learning for Relation Classification from Noisy Data

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Regularizing Class-wise Predictions via Self-knowledge Distillation

Domain-Adversarial Neural Networks

Structured Pruning of Large Language Models

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning

Distributionally Robust Logistic Regression

Neural Cognitive Diagnosis for Intelligent Education Systems

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model

Adversarial Removal of Demographic Attributes from Text Data

Remaining Useful Lifetime Prediction via Deep Domain Adaptation

Quant GANs: Deep Generation of Financial Time Series

Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

Generative Adversarial Active Learning for Unsupervised Outlier Detection

Parametric UMAP embeddings for representation and semi-supervised learning

A New Approach to Probabilistic Programming Inference

Supermasks in Superposition

Graph Information Bottleneck

SE(3) diffusion model with application to protein backbone generation

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

Latent ODEs for Irregularly-Sampled Time Series

Adversarial Attacks and Defences Competition

Deep learning for molecular design - a review of the state of the art