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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601 to 660 of 6,790

ProteinNet: a standardized data set for machine learning of protein structure
Mohammed AlQuraishi
q-bio.BMcs.LGq-bio.QMarXiv:1902.00249v12019
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
cs.LGcs.CVcs.ITarXiv:2105.10446v32021
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani, James Zou
stat.MLcs.CVcs.LGarXiv:2002.09815v32020
Representational Strengths and Limitations of Transformers
Clayton Sanford, Daniel Hsu, Matus Telgarsky
cs.LGstat.MLarXiv:2306.02896v22023
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi +4
cs.LGcs.CVeess.IVarXiv:2001.04271v22020
Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Philippe Preux +3
cs.LGstat.MLarXiv:1510.08231v32015
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Gen Li, Yuting Wei, Yuejie Chi +1
cs.LGcs.ITmath.OCarXiv:2005.12900v82020
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu
cs.LGcs.CRcs.DSarXiv:2110.04995v22021
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search
Arber Zela, Julien Siems, Frank Hutter
cs.LGcs.CVcs.NEarXiv:2001.10422v22020
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
Julia Westermayr, Michael Gastegger, Philipp Marquetand
physics.chem-phcs.LGstat.MLarXiv:2002.07264v12020
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich, Devin Willmott, Qiang Ye
stat.MLcs.LGarXiv:1707.09520v32017
A Survey of Deep Learning for Scientific Discovery
Maithra Raghu, Eric Schmidt
cs.LGstat.MLarXiv:2003.11755v12020
Multi-View Matrix Completion for Multi-Label Image Classification
Yong Luo, Tongliang Liu, Dacheng Tao +1
stat.MLcs.CVcs.LGarXiv:1904.03901v12019
Data-driven discovery of PDEs in complex datasets
Jens Berg, Kaj Nyström
stat.MLcs.LGmath.NAarXiv:1808.10788v12018
Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review
Sourya Sengupta, Amitojdeep Singh, Henry A. Leopold +2
cs.CVcs.LGstat.MLarXiv:1812.07101v32018
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Laura Swiler, Mamikon Gulian, Ari Frankel +2
cs.LGmath.STstat.MLarXiv:2006.09319v32020
QUBO Formulations for Training Machine Learning Models
Prasanna Date, Davis Arthur, Lauren Pusey-Nazzaro
cs.LGphysics.data-anstat.MLarXiv:2008.02369v12020
Multilevel Clustering via Wasserstein Means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin +3
stat.MLstat.COstat.MEarXiv:1706.03883v12017
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
Jun Zhu, Ning Chen, Eric P. Xing
cs.LGcs.AIstat.MEarXiv:1210.1766v32012
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
Hao Peng, Jianxin Li, Qiran Gong +5
cs.IRcs.LGstat.MLarXiv:1906.04898v12019
A Comparative Study of Collaborative Filtering Algorithms
Joonseok Lee, Mingxuan Sun, Guy Lebanon
cs.IRstat.MLarXiv:1205.3193v12012
Distilled Sensing: Adaptive Sampling for Sparse Detection and Estimation
Jarvis Haupt, Rui Castro, Robert Nowak
math.STcs.ITstat.MLarXiv:1001.5311v22010
A statistical approach to bias in zero-shot learning: the lens of handwriting recognition
Clarence Chew, Gim Siang Chia, Sukalpa Chanda +2
stat.MLcs.AIcs.CVarXiv:2609.10084v12026
Predicting Hurricane Trajectories using a Recurrent Neural Network
Sheila Alemany, Jonathan Beltran, Adrian Perez +1
cs.LGcs.AIcs.CYarXiv:1802.02548v32018
Bayesian Hypernetworks
David Krueger, Chin-Wei Huang, Riashat Islam +3
stat.MLcs.AIcs.LGarXiv:1710.04759v22017
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lucic, Xiaohua Zhai +2
cs.LGstat.MLarXiv:1807.04720v32018
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran +2
cs.LGstat.MLarXiv:1903.06661v12019
Hard negative examples are hard, but useful
Hong Xuan, Abby Stylianou, Xiaotong Liu +1
cs.CVcs.LGstat.MLarXiv:2007.12749v22020
FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models
Yansen Han, Shengyi Liao, Peng Sun +4
stat.MLcs.AIcs.CVarXiv:2609.09905v12026
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Risheng Liu, Pan Mu, Xiaoming Yuan +2
cs.LGcs.CVmath.DSarXiv:2006.04045v22020
A Guide to Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
hep-phphysics.data-anstat.MLarXiv:1805.00020v42018
Multimodal Task-Driven Dictionary Learning for Image Classification
Soheil Bahrampour, Nasser M. Nasrabadi, Asok Ray +1
stat.MLcs.CVcs.LGarXiv:1502.01094v22015
A Distributional Framework for Data Valuation
Amirata Ghorbani, Michael P. Kim, James Zou
cs.LGstat.MLarXiv:2002.12334v12020
Deep Exponential Families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin +1
stat.MLcs.LGarXiv:1411.2581v12014
Large-Scale Convex Minimization with a Low-Rank Constraint
Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
cs.LGstat.MLarXiv:1106.1622v12011
B-Spline CNNs on Lie Groups
Erik J Bekkers
cs.LGstat.MLarXiv:1909.12057v42019
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin +4
cs.CRcs.LGstat.MLarXiv:2204.00032v22022
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
Itay Hubara, Yury Nahshan, Yair Hanani +2
cs.LGstat.MLarXiv:2006.10518v22020
Trivializations for Gradient-Based Optimization on Manifolds
Mario Lezcano-Casado
cs.LGstat.MLarXiv:1909.09501v22019
Primal Wasserstein Imitation Learning
Robert Dadashi, Léonard Hussenot, Matthieu Geist +1
cs.LGstat.MLarXiv:2006.04678v22020
A Brief Introduction to Machine Learning for Engineers
Osvaldo Simeone
cs.LGcs.ITstat.MLarXiv:1709.02840v32017
Learning Transferable Cooperative Behavior in Multi-Agent Teams
Akshat Agarwal, Sumit Kumar, Katia Sycara
cs.LGcs.MAstat.MLarXiv:1906.01202v12019
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei +1
cs.LGcs.AIstat.MLarXiv:1811.03056v32018
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
cs.CVcs.LGstat.MLarXiv:1904.07457v12019
Sparse Recovery of Streaming Signals Using L1-Homotopy
M. Salman Asif, Justin Romberg
cs.ITmath.OCstat.MLarXiv:1306.3331v12013
Path Planning using Neural A* Search
Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain +2
cs.LGcs.AIstat.MLarXiv:2009.07476v32020
Sparse Stochastic Inference for Latent Dirichlet allocation
David Mimno, Matt Hoffman, David Blei
cs.LGstat.MLarXiv:1206.6425v12012
Policy Evaluation and Optimization with Continuous Treatments
Nathan Kallus, Angela Zhou
stat.MLcs.LGarXiv:1802.06037v12018
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy +1
cs.LGstat.MLarXiv:1911.05815v12019
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi
cs.CLcs.LGstat.MLarXiv:2009.10195v22020
On the Sample Complexity of Reinforcement Learning with a Generative Model
Mohammad Gheshlaghi Azar, Remi Munos, Bert Kappen
cs.LGstat.MLarXiv:1206.6461v12012
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals
Emilie Kaufmann, Wouter Koolen
stat.MLcs.LGarXiv:1811.11419v22018
Variance Reduced Stochastic Gradient Descent with Neighbors
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien +1
cs.LGmath.OCstat.MLarXiv:1506.03662v42015
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis +1
cs.LGmath.DSmath.NAarXiv:2211.08939v32022
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
cs.LGcs.AIcs.GTarXiv:1810.09026v52018
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
Janis Postels, Francesco Ferroni, Huseyin Coskun +2
cs.LGstat.MLarXiv:1908.00598v32019
GENIE: Higher-Order Denoising Diffusion Solvers
Tim Dockhorn, Arash Vahdat, Karsten Kreis
stat.MLcs.LGarXiv:2210.05475v12022
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
Aaron Griffith, Andrew Pomerance, Daniel J. Gauthier
cs.LGnlin.CDstat.MLarXiv:1910.00659v22019
Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning
Krzysztof M. Graczyk, Maciej Matyka
physics.comp-phcond-mat.dis-nncs.LGarXiv:2007.02820v12020
Learning with Synthetic Data via SGD in High-Dimensional Linear Regression
Jichu li, Difan Zou
stat.MLcs.AIcs.LGarXiv:2609.09572v12026

Machine Learning (stat)

ProteinNet: a standardized data set for machine learning of protein structure

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

Neuron Shapley: Discovering the Responsible Neurons

Representational Strengths and Limitations of Transformers

Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

Operator-valued Kernels for Learning from Functional Response Data

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

The Skellam Mechanism for Differentially Private Federated Learning

NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search

Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics

Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

A Survey of Deep Learning for Scientific Discovery

Multi-View Matrix Completion for Multi-Label Image Classification

Data-driven discovery of PDEs in complex datasets

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

QUBO Formulations for Training Machine Learning Models

Multilevel Clustering via Wasserstein Means

Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

A Comparative Study of Collaborative Filtering Algorithms

Distilled Sensing: Adaptive Sampling for Sparse Detection and Estimation

A statistical approach to bias in zero-shot learning: the lens of handwriting recognition

Predicting Hurricane Trajectories using a Recurrent Neural Network

Bayesian Hypernetworks

A Large-Scale Study on Regularization and Normalization in GANs

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Hard negative examples are hard, but useful

FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models

A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton

A Guide to Constraining Effective Field Theories with Machine Learning

Multimodal Task-Driven Dictionary Learning for Image Classification

A Distributional Framework for Data Valuation

Deep Exponential Families

Large-Scale Convex Minimization with a Low-Rank Constraint

B-Spline CNNs on Lie Groups

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets

Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming

Trivializations for Gradient-Based Optimization on Manifolds

Primal Wasserstein Imitation Learning

A Brief Introduction to Machine Learning for Engineers

Learning Transferable Cooperative Behavior in Multi-Agent Teams

Policy Certificates: Towards Accountable Reinforcement Learning

A Bayesian Perspective on the Deep Image Prior

Sparse Recovery of Streaming Signals Using L1-Homotopy

Path Planning using Neural A* Search

Sparse Stochastic Inference for Latent Dirichlet allocation

Policy Evaluation and Optimization with Continuous Treatments

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

On the Sample Complexity of Reinforcement Learning with a Generative Model

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals

Variance Reduced Stochastic Gradient Descent with Neighbors

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation

GENIE: Higher-Order Denoising Diffusion Solvers

Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers

Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning

Learning with Synthetic Data via SGD in High-Dimensional Linear Regression