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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121 to 180 of 6,772

Generalized Score Matching for Parameter Estimation on Convex Domains
Nishanth Shetty, Saisuchith Mahajan, Chandra Sekhar Seelamantula
cs.LGstat.MLarXiv:2609.11521v12026
Review of Swarm Intelligence-based Feature Selection Methods
Mehrdad Rostami, Kamal Berahmand, Saman Forouzandeh
cs.LGcs.NEstat.MLarXiv:2008.04103v12020
Predicting Process Behaviour using Deep Learning
Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
cs.LGstat.MLarXiv:1612.04600v22016
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal +1
cs.LGcs.ROstat.MLarXiv:2006.09359v62020
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
A. F. M. Shahab Uddin, Mst. Sirazam Monira, Wheemyung Shin +2
cs.LGstat.MLarXiv:2006.01791v22020
Explainable Reinforcement Learning: A Survey
Erika Puiutta, Eric MSP Veith
cs.LGstat.MLarXiv:2005.06247v12020
Adversarial Machine Learning in Network Intrusion Detection Systems
Elie Alhajjar, Paul Maxwell, Nathaniel D. Bastian
cs.CRcs.LGcs.NEarXiv:2004.11898v12020
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Zixiang Chen, Yihe Deng, Huizhuo Yuan +2
cs.LGcs.AIcs.CLarXiv:2401.01335v32024
Training with Quantization Noise for Extreme Model Compression
Angela Fan, Pierre Stock, Benjamin Graham +4
cs.LGstat.MLarXiv:2004.07320v32020
Diffusion Schrödinger Bridge Matching
Yuyang Shi, Valentin De Bortoli, Andrew Campbell +1
stat.MLcs.LGarXiv:2303.16852v32023
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin +5
cs.LGcs.AIcs.CLarXiv:2303.06296v22023
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
Amir Mosavi, Pedram Ghamisi, Yaser Faghan +1
q-fin.STcs.LGecon.GNarXiv:2004.01509v12020
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
cs.CVcs.LGstat.MLarXiv:2003.08802v12020
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez +3
cs.SIcs.LGphysics.soc-pharXiv:1507.02293v22015
A Survey on The Expressive Power of Graph Neural Networks
Ryoma Sato
cs.LGstat.MLarXiv:2003.04078v42020
Statistical power for cluster analysis
E. S. Dalmaijer, C. L. Nord, D. E. Astle
stat.MLcs.LGq-bio.QMarXiv:2003.00381v32020
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
Jonathan Frankle, David J. Schwab, Ari S. Morcos
cs.LGcs.AIcs.NEarXiv:2003.00152v32020
Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal +3
cs.LGcs.CVstat.MLarXiv:2002.09437v22020
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination
Olga Kolchyna, Tharsis T. P. Souza, Philip Treleaven +1
cs.CLcs.IRcs.LGarXiv:1507.00955v32015
Data-Free Adversarial Distillation
Gongfan Fang, Jie Song, Chengchao Shen +3
cs.LGcs.CVstat.MLarXiv:1912.11006v32019
Orthogonal Gradient Descent for Continual Learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott +1
cs.LGstat.MLarXiv:1910.07104v12019
Soft-Label Dataset Distillation and Text Dataset Distillation
Ilia Sucholutsky, Matthias Schonlau
cs.LGcs.AIstat.MLarXiv:1910.02551v32019
Hamiltonian Generative Networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle +3
cs.LGstat.MLarXiv:1909.13789v22019
Deep Equilibrium Models
Shaojie Bai, J. Zico Kolter, Vladlen Koltun
cs.LGstat.MLarXiv:1909.01377v22019
Lookahead Optimizer: k steps forward, 1 step back
Michael R. Zhang, James Lucas, Geoffrey Hinton +1
cs.LGcs.NEstat.MLarXiv:1907.08610v22019
Interpretable Counterfactual Explanations Guided by Prototypes
Arnaud Van Looveren, Janis Klaise
cs.LGstat.MLarXiv:1907.02584v22019
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Marc Brockschmidt
cs.LGstat.MLarXiv:1906.12192v52019
On Physical Adversarial Patches for Object Detection
Mark Lee, Zico Kolter
cs.CVcs.CRcs.LGarXiv:1906.11897v12019
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole +2
cs.LGcs.CVstat.MLarXiv:1906.02611v12019
Pylearn2: a machine learning research library
Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin +6
stat.MLcs.LGcs.MSarXiv:1308.4214v12013
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber +1
cs.LGcs.CVcs.NEarXiv:1905.12506v32019
Exploiting Cognitive Structure for Adaptive Learning
Qi Liu, Shiwei Tong, Chuanren Liu +4
cs.CYcs.LGstat.MLarXiv:1905.12470v12019
Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj +2
cs.LGcs.CVstat.MLarXiv:1905.08114v12019
Ranking and combining multiple predictors without labeled data
Fabio Parisi, Francesco Strino, Boaz Nadler +1
stat.MLcs.LGarXiv:1303.3257v32013
Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning
Jing Lin, Marcel Nassar, Brian L. Evans
stat.MLcs.ITarXiv:1303.1217v12013
Explainable AI for Trees: From Local Explanations to Global Understanding
Scott M. Lundberg, Gabriel Erion, Hugh Chen +7
cs.LGcs.AIstat.MLarXiv:1905.04610v12019
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Ziyu Wang, Frank Hutter, Masrour Zoghi +2
stat.MLcs.LGarXiv:1301.1942v22013
Predictive Inequity in Object Detection
Benjamin Wilson, Judy Hoffman, Jamie Morgenstern
cs.CVcs.LGstat.MLarXiv:1902.11097v12019
Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson +3
cs.LGstat.MLarXiv:1902.06977v12019
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization
Nicolas Gillis, Stephen A. Vavasis
stat.MLcs.LGmath.OCarXiv:1208.1237v32012
Variational Bayesian Inference with Stochastic Search
John Paisley, David Blei, Michael Jordan
cs.LGstat.COstat.MLarXiv:1206.6430v12012
Combining Physically-Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn from Mismatch?
Alexander Y. Sun, Bridget R. Scanlon, Zizhan Zhang +4
physics.geo-phcs.LGstat.MLarXiv:1902.01933v12019
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy +4
cs.ROcs.LGstat.MLarXiv:1901.08652v12019
Sparse Subspace Clustering: Algorithm, Theory, and Applications
Ehsan Elhamifar, Rene Vidal
cs.CVcs.IRcs.ITarXiv:1203.1005v32012
Generalized Fisher Score for Feature Selection
Quanquan Gu, Zhenhui Li, Jiawei Han
cs.LGstat.MLarXiv:1202.3725v12012
Practical Lossless Compression with Latent Variables using Bits Back Coding
James Townsend, Tom Bird, David Barber
cs.LGcs.AIcs.ITarXiv:1901.04866v12019
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
Aditya Kusupati, Manish Singh, Kush Bhatia +3
cs.LGcs.AIcs.NEarXiv:1901.02358v12019
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu +6
cs.LGcs.AIstat.MLarXiv:1812.08434v62018
Statistical Topic Models for Multi-Label Document Classification
Timothy N. Rubin, America Chambers, Padhraic Smyth +1
stat.MLcs.LGarXiv:1107.2462v22011
Improving Robustness using Generated Data
Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles +3
cs.LGcs.CVstat.MLarXiv:2110.09468v22021
Self-supervised Learning is More Robust to Dataset Imbalance
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon +1
cs.LGcs.CVstat.MLarXiv:2110.05025v22021
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Titouan Vayer, Laetita Chapel, Rémi Flamary +2
stat.MLcs.LGarXiv:1811.02834v12018
GANs for Medical Image Analysis
Salome Kazeminia, Christoph Baur, Arjan Kuijper +4
cs.CVcs.LGstat.MLarXiv:1809.06222v32018
Don't Use Large Mini-Batches, Use Local SGD
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel +1
cs.LGstat.MLarXiv:1808.07217v62018
Summaries:한국어
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Mostafa Karimi, Di Wu, Zhangyang Wang +1
q-bio.BMcs.LGstat.MLarXiv:1806.07537v22018
Neural Code Comprehension: A Learnable Representation of Code Semantics
Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
cs.LGcs.NEcs.PLarXiv:1806.07336v32018
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Edouard Grave, Armand Joulin, Quentin Berthet
cs.LGcs.CLstat.MLarXiv:1805.11222v12018
A tutorial on conformal prediction
Glenn Shafer, Vladimir Vovk
cs.LGstat.MLarXiv:0706.3188v12007
Sample Complexity of Multi-task Reinforcement Learning
Emma Brunskill, Lihong Li
cs.LGstat.MLarXiv:1309.6821v12013
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
Shashank Rajput, Hongyi Wang, Zachary Charles +1
cs.LGcs.DCstat.MLarXiv:1907.12205v22019

Machine Learning (stat)

Generalized Score Matching for Parameter Estimation on Convex Domains

Review of Swarm Intelligence-based Feature Selection Methods

Predicting Process Behaviour using Deep Learning

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

Explainable Reinforcement Learning: A Survey

Adversarial Machine Learning in Network Intrusion Detection Systems

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Training with Quantization Noise for Extreme Model Compression

Diffusion Schrödinger Bridge Matching

Stabilizing Transformer Training by Preventing Attention Entropy Collapse

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

A Survey on The Expressive Power of Graph Neural Networks

Statistical power for cluster analysis

Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs

Calibrating Deep Neural Networks using Focal Loss

Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination

Data-Free Adversarial Distillation

Orthogonal Gradient Descent for Continual Learning

Soft-Label Dataset Distillation and Text Dataset Distillation

Hamiltonian Generative Networks

Deep Equilibrium Models

Lookahead Optimizer: k steps forward, 1 step back

Interpretable Counterfactual Explanations Guided by Prototypes

GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

On Physical Adversarial Patches for Object Detection

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

Pylearn2: a machine learning research library

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

Exploiting Cognitive Structure for Adaptive Learning

Zero-Shot Knowledge Distillation in Deep Networks

Ranking and combining multiple predictors without labeled data

Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

Explainable AI for Trees: From Local Explanations to Global Understanding

Bayesian Optimization in a Billion Dimensions via Random Embeddings

Predictive Inequity in Object Detection

Evaluating model calibration in classification

Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization

Variational Bayesian Inference with Stochastic Search

Combining Physically-Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn from Mismatch?

Learning agile and dynamic motor skills for legged robots

Sparse Subspace Clustering: Algorithm, Theory, and Applications

Generalized Fisher Score for Feature Selection

Practical Lossless Compression with Latent Variables using Bits Back Coding

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

Graph Neural Networks: A Review of Methods and Applications

Statistical Topic Models for Multi-Label Document Classification

Improving Robustness using Generated Data

Self-supervised Learning is More Robust to Dataset Imbalance

Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties

GANs for Medical Image Analysis

Don't Use Large Mini-Batches, Use Local SGD

DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

Neural Code Comprehension: A Learnable Representation of Code Semantics

Unsupervised Alignment of Embeddings with Wasserstein Procrustes

A tutorial on conformal prediction

Sample Complexity of Multi-task Reinforcement Learning

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation