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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5,521 to 5,580 of 6,784

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma +1
cs.LGcs.CLstat.MLarXiv:1906.01195v12019
Geometric GAN
Jae Hyun Lim, Jong Chul Ye
stat.MLcond-mat.dis-nncs.AIarXiv:1705.02894v22017
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
Huy Phan, Fernando Andreotti, Navin Cooray +2
cs.LGeess.SPstat.MLarXiv:1809.10932v32018
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Arild Nøkland
stat.MLcs.LGarXiv:1609.01596v52016
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton +4
cs.LGstat.MLarXiv:1803.01442v12018
Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data
Ian Hultman, Kirtikanth Kalapatapu, Yassine Filali +2
stat.MLcs.LGstat.COarXiv:2608.21551v12026
A Data-Driven Approach to State Construction in Markov Models
Linde Van Gestel, Marie-Anne Guerry, Evy Rombaut
stat.MLcs.LGmath.PRarXiv:2608.21480v12026
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
Jing Wang, HaiYing Wang, Qiang Zhang +1
stat.MLcs.LGarXiv:2608.22597v12026
Generalizing Across Domains via Cross-Gradient Training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti +3
cs.LGstat.MLarXiv:1804.10745v22018
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen, Daphna Weinshall
cs.LGstat.MLarXiv:1904.03626v32019
Stochastic Video Generation with a Learned Prior
Remi Denton, Rob Fergus
cs.CVcs.AIcs.LGarXiv:1802.07687v22018
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos +5
cs.CVcs.LGstat.MLarXiv:1907.07484v22019
Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra +6
cs.LGcs.CVstat.MLarXiv:1903.00450v32019
Attention in Natural Language Processing
Andrea Galassi, Marco Lippi, Paolo Torroni
cs.CLcs.AIcs.LGarXiv:1902.02181v42019
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
Risi Kondor, Shubhendu Trivedi
stat.MLcs.LGarXiv:1802.03690v32018
Learning from Noisy Labels with Distillation
Yuncheng Li, Jianchao Yang, Yale Song +3
cs.CVcs.LGstat.MLarXiv:1703.02391v22017
Training generative neural networks via Maximum Mean Discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
stat.MLcs.LGarXiv:1505.03906v12015
Implicit Regularization in Matrix Factorization
Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli +2
stat.MLcs.LGarXiv:1705.09280v12017
Conditional Time Series Forecasting with Convolutional Neural Networks
Anastasia Borovykh, Sander Bohte, Cornelis W. Oosterlee
stat.MLarXiv:1703.04691v52017
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach +6
cs.IRcs.LGstat.MLarXiv:1901.09451v12019
Steerable CNNs
Taco S. Cohen, Max Welling
cs.LGstat.MLarXiv:1612.08498v12016
Summaries:한국어
Recovering Weighted Tangent Geometry from a Single-Scale Score Field
Ziqi Zhao, Qingjian Ni
stat.MLcs.LGarXiv:2608.22334v12026
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Yeming Wen, Dustin Tran, Jimmy Ba
cs.LGstat.MLarXiv:2002.06715v22020
(More) Efficient Reinforcement Learning via Posterior Sampling
Ian Osband, Daniel Russo, Benjamin Van Roy
stat.MLcs.LGarXiv:1306.0940v52013
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Micah J Sheller, G Anthony Reina, Brandon Edwards +2
cs.LGstat.MLarXiv:1810.04304v22018
Differentially Private Generative Adversarial Network
Liyang Xie, Kaixiang Lin, Shu Wang +2
cs.LGcs.CRstat.MLarXiv:1802.06739v12018
Replicable Conformal Prediction
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
stat.MLcs.LGarXiv:2608.23638v12026
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
cs.LGmath.OCmath.PRarXiv:1702.03849v32017
Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski +4
cs.LGstat.MLarXiv:2003.13350v12020
Real-Time Adaptive Image Compression
Oren Rippel, Lubomir Bourdev
stat.MLcs.CVcs.LGarXiv:1705.05823v12017
Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik, Jure Leskovec
cs.LGcs.SIq-bio.MNarXiv:1707.04638v12017
Maximum a Posteriori Policy Optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa +3
cs.LGcs.AIcs.ITarXiv:1806.06920v12018
Training GANs with Optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis +1
cs.LGcs.GTstat.MLarXiv:1711.00141v22017
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan, Luke Vilnis, Quoc V. Le +4
stat.MLcs.LGarXiv:1511.06807v12015
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Izhar Wallach, Michael Dzamba, Abraham Heifets
cs.LGcs.NEq-bio.BMarXiv:1510.02855v12015
Imagination-Augmented Agents for Deep Reinforcement Learning
Théophane Weber, Sébastien Racanière, David P. Reichert +12
cs.LGcs.AIstat.MLarXiv:1707.06203v22017
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
Marinka Zitnik, Francis Nguyen, Bo Wang +3
q-bio.QMcs.CEcs.LGarXiv:1807.00123v22018
How to learn a graph from smooth signals
Vassilis Kalofolias
stat.MLcs.LGphysics.data-anarXiv:1601.02513v12016
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Nan Wu, Jason Phang, Jungkyu Park +29
cs.LGcs.CVstat.MLarXiv:1903.08297v12019
Communication Efficient Distributed Optimization using an Approximate Newton-type Method
Ohad Shamir, Nathan Srebro, Tong Zhang
cs.LGmath.OCstat.MLarXiv:1312.7853v42013
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
Qibin Zhao, Liqing Zhang, Andrzej Cichocki
cs.LGcs.CVstat.MLarXiv:1401.6497v22014
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Wieland Brendel, Matthias Bethge
cs.CVcs.LGstat.MLarXiv:1904.00760v12019
Universality, Characteristic Kernels and RKHS Embedding of Measures
Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet
stat.MLmath.STarXiv:1003.0887v12010
Protein-Ligand Scoring with Convolutional Neural Networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo +2
stat.MLcs.LGq-bio.BMarXiv:1612.02751v12016
Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Vitaly Shmatikov
cs.LGcs.CRstat.MLarXiv:1905.12101v22019
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach, Nir Shlezinger, Xiaoyong Ni +3
eess.SPcs.LGstat.MLarXiv:2107.10043v32021
Ansor: Generating High-Performance Tensor Programs for Deep Learning
Lianmin Zheng, Chengfan Jia, Minmin Sun +9
cs.LGcs.NEcs.PFarXiv:2006.06762v52020
Visual Reinforcement Learning with Imagined Goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal +3
cs.LGcs.CVcs.ROarXiv:1807.04742v22018
Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining
Yicheng Mao, Hongru Du
cs.AIstat.MLarXiv:2608.23922v12026
The Space of Transferable Adversarial Examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow +2
stat.MLcs.CRcs.LGarXiv:1704.03453v22017
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
Trevor Hastie, Rahul Mazumder, Jason Lee +1
stat.MEstat.MLarXiv:1410.2596v12014
Analyzing Inverse Problems with Invertible Neural Networks
Lynton Ardizzone, Jakob Kruse, Sebastian Wirkert +6
cs.LGstat.MLarXiv:1808.04730v32018
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
cs.LGcs.AIcs.CVarXiv:1711.00848v32017
Learning Invariant Representations for Reinforcement Learning without Reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra +2
cs.LGcs.AIstat.MLarXiv:2006.10742v22020
Counterfactual Visual Explanations
Yash Goyal, Ziyan Wu, Jan Ernst +3
cs.LGcs.AIcs.CVarXiv:1904.07451v22019
Deep learning for neuroimaging: a validation study
Sergey M. Plis, Devon R. Hjelm, Ruslan Salakhutdinov +1
cs.NEcs.LGstat.MLarXiv:1312.5847v32013
Universality of Deep Convolutional Neural Networks
Ding-Xuan Zhou
cs.LGstat.MLarXiv:1805.10769v22018
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov +2
cs.LGcs.AIcs.CVarXiv:1803.05407v32018
Curiosity-driven Exploration by Self-supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros +1
cs.LGcs.AIcs.CVarXiv:1705.05363v12017
A Random Forest Guided Tour
Gérard Biau, Erwan Scornet
math.STstat.MLarXiv:1511.05741v12015

Machine Learning (stat)

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Geometric GAN

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Direct Feedback Alignment Provides Learning in Deep Neural Networks

Stochastic Activation Pruning for Robust Adversarial Defense

Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data

A Data-Driven Approach to State Construction in Markov Models

Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models

Generalizing Across Domains via Cross-Gradient Training

On The Power of Curriculum Learning in Training Deep Networks

Stochastic Video Generation with a Learned Prior

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Multi-Object Representation Learning with Iterative Variational Inference

Attention in Natural Language Processing

On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups

Learning from Noisy Labels with Distillation

Training generative neural networks via Maximum Mean Discrepancy optimization

Implicit Regularization in Matrix Factorization

Conditional Time Series Forecasting with Convolutional Neural Networks

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting

Steerable CNNs

Recovering Weighted Tangent Geometry from a Single-Scale Score Field

BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

(More) Efficient Reinforcement Learning via Posterior Sampling

Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

Differentially Private Generative Adversarial Network

Replicable Conformal Prediction

Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

Agent57: Outperforming the Atari Human Benchmark

Real-Time Adaptive Image Compression

Predicting multicellular function through multi-layer tissue networks

Maximum a Posteriori Policy Optimisation

Training GANs with Optimism

Adding Gradient Noise Improves Learning for Very Deep Networks

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

Imagination-Augmented Agents for Deep Reinforcement Learning

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

How to learn a graph from smooth signals

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Communication Efficient Distributed Optimization using an Approximate Newton-type Method

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Universality, Characteristic Kernels and RKHS Embedding of Measures

Protein-Ligand Scoring with Convolutional Neural Networks

Differential Privacy Has Disparate Impact on Model Accuracy

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Ansor: Generating High-Performance Tensor Programs for Deep Learning

Visual Reinforcement Learning with Imagined Goals

Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining

The Space of Transferable Adversarial Examples

Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

Analyzing Inverse Problems with Invertible Neural Networks

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Learning Invariant Representations for Reinforcement Learning without Reconstruction

Counterfactual Visual Explanations

Deep learning for neuroimaging: a validation study

Universality of Deep Convolutional Neural Networks

Averaging Weights Leads to Wider Optima and Better Generalization

Curiosity-driven Exploration by Self-supervised Prediction

A Random Forest Guided Tour