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)

5,761 to 5,820 of 6,797

Hyper-Parameter Optimization: A Review of Algorithms and Applications
Tong Yu, Hong Zhu
cs.LGstat.MLarXiv:2003.05689v12020
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang +2
cs.LGcs.SIstat.MLarXiv:2006.15437v12020
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien +2
cs.LGstat.MLarXiv:1904.12787v22019
Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas +2
cs.AIcs.LGcs.NEarXiv:1602.02867v42016
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
cs.AIcs.LGmath.AParXiv:1711.10566v12017
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden +1
cs.LGstat.MLarXiv:1905.05134v22019
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Aaron Lou, Chenlin Meng, Stefano Ermon
stat.MLcs.CLcs.LGarXiv:2310.16834v32023
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia +4
cs.LGastro-ph.COastro-ph.IMarXiv:2006.11287v22020
Graph Representation Learning via Graphical Mutual Information Maximization
Zhen Peng, Wenbing Huang, Minnan Luo +4
cs.LGcs.AIstat.MLarXiv:2002.01169v12020
A Survey on Causal Inference
Liuyi Yao, Zhixuan Chu, Sheng Li +3
stat.MEcs.AIcs.LGarXiv:2002.02770v12020
Machine Learning in IoT Security: Current Solutions and Future Challenges
Fatima Hussain, Rasheed Hussain, Syed Ali Hassan +1
cs.CRcs.LGstat.MLarXiv:1904.05735v12019
BEHRT: Transformer for Electronic Health Records
Yikuan Li, Shishir Rao, Jose Roberto Ayala Solares +5
cs.LGstat.MLarXiv:1907.09538v12019
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Rebecca L. Russell, Louis Kim, Lei H. Hamilton +5
cs.LGcs.AIcs.SEarXiv:1807.04320v22018
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang +5
cs.LGstat.MLarXiv:1711.08267v12017
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou
cs.LGstat.MLarXiv:2208.06193v32022
Structural Deep Clustering Network
Deyu Bo, Xiao Wang, Chuan Shi +3
cs.LGstat.MLarXiv:2002.01633v32020
Professor Forcing: A New Algorithm for Training Recurrent Networks
Alex Lamb, Anirudh Goyal, Ying Zhang +3
stat.MLcs.LGarXiv:1610.09038v12016
Scaling DoRA: High-Rank Adaptation via Factored Norms and Fused Kernels
Alexandra Zelenin, Alexandra Zhuravlyova
cs.LGstat.MLarXiv:2603.22276v12026
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain +2
stat.MLcs.LGarXiv:2111.14522v32021
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek
cs.AIcs.LGstat.MLarXiv:2007.15911v22020
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
cs.LGcs.AIstat.MLarXiv:1703.03717v22017
Explainable Prediction of Medical Codes from Clinical Text
James Mullenbach, Sarah Wiegreffe, Jon Duke +2
cs.CLcs.LGstat.MLarXiv:1802.05695v22018
Provably Efficient Reinforcement Learning with Linear Function Approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang +1
cs.LGmath.OCstat.MLarXiv:1907.05388v22019
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K. Warfield +1
cs.CVcs.LGeess.IVarXiv:1912.02911v42019
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao +1
cs.LGcs.AIstat.MLarXiv:1904.10098v12019
Optimal Ratio for Data Splitting
V. Roshan Joseph
stat.MLcs.LGarXiv:2202.03326v12022
Online Continual Learning with Maximally Interfered Retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky +4
cs.LGstat.MLarXiv:1908.04742v32019
SuperSpike: Supervised learning in multi-layer spiking neural networks
Friedemann Zenke, Surya Ganguli
q-bio.NCcs.LGcs.NEarXiv:1705.11146v22017
Data Predictability Shapes Weibull Weight-Scale Growth in Transformer Training
Tiexin Ding
cs.LGstat.MLarXiv:2608.23573v12026
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
Shakir Mohamed, Marie-Therese Png, William Isaac
cs.CYcs.AIcs.LGarXiv:2007.04068v12020
Explainable Machine Learning in Deployment
Umang Bhatt, Alice Xiang, Shubham Sharma +7
cs.LGcs.AIcs.CYarXiv:1909.06342v42019
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Juntao Fang, Shifeng Xie, Ruichu Cai +6
cs.LGcs.AIstat.MLarXiv:2608.24033v12026
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Sampo Kuutti, Richard Bowden, Yaochu Jin +2
cs.LGcs.CVeess.SYarXiv:1912.10773v12019
Measuring Calibration in Deep Learning
Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel +4
cs.LGstat.MLarXiv:1904.01685v22019
SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng, Tao Fan, Yilun Jin +4
cs.LGstat.MLarXiv:1901.08755v32019
Malware Detection by Eating a Whole EXE
Edward Raff, Jon Barker, Jared Sylvester +3
stat.MLcs.CRcs.LGarXiv:1710.09435v12017
Learning Deep Generative Models of Graphs
Yujia Li, Oriol Vinyals, Chris Dyer +2
cs.LGstat.MLarXiv:1803.03324v12018
Applications of Deep Learning and Reinforcement Learning to Biological Data
Mufti Mahmud, M. Shamim Kaiser, Amir Hussain +1
cs.LGstat.MLarXiv:1711.03985v22017
The Computational Limits of Deep Learning
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee +1
cs.LGstat.MLarXiv:2007.05558v22020
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Kevin Scaman, Aladin Virmaux
stat.MLcs.LGarXiv:1805.10965v22018
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
cs.CVcs.LGeess.IVarXiv:2006.10511v22020
Improving Diffusion Models for Inverse Problems using Manifold Constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu +1
cs.LGcs.AIcs.CVarXiv:2206.00941v32022
A review of machine learning applications in wildfire science and management
Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian +3
cs.LGstat.MLarXiv:2003.00646v22020
Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar +3
cs.LGcs.CVstat.MLarXiv:2007.00644v22020
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li +5
cs.LGcs.CVstat.MLarXiv:1912.08795v22019
Certified Data Removal from Machine Learning Models
Chuan Guo, Tom Goldstein, Awni Hannun +1
cs.LGstat.MLarXiv:1911.03030v62019
The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling
Yaoshiang Ho, Samuel Wookey
cs.LGcs.AIstat.MLarXiv:2001.00570v12020
Improved denoising diffusion probabilistic models with efficient non-diagonal covariance modeling
Rui Xia, Ayan Das, Artem Artemev +3
cs.CVstat.MLarXiv:2608.21972v12026
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Souhaib Ben Taieb, Gianluca Bontempi, Amir Atiya +1
stat.MLcs.AIcs.LGarXiv:1108.3259v12011
Artificial Entanglement in the Fine-Tuning of Large Language Models
Min Chen, Zihan Wang, Canyu Chen +3
cs.LGcs.AIhep-tharXiv:2601.06788v12026
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang +9
cs.SEcs.CRcs.LGarXiv:1803.07519v42018
Variational Lossy Autoencoder
Xi Chen, Diederik P. Kingma, Tim Salimans +5
cs.LGstat.MLarXiv:1611.02731v22016
Learning from positive and unlabeled data: a survey
Jessa Bekker, Jesse Davis
cs.LGstat.MLarXiv:1811.04820v32018
Simple Black-box Adversarial Attacks
Chuan Guo, Jacob R. Gardner, Yurong You +2
cs.LGcs.CRstat.MLarXiv:1905.07121v22019
A Survey on Metric Learning for Feature Vectors and Structured Data
Aurélien Bellet, Amaury Habrard, Marc Sebban
cs.LGcs.AIstat.MLarXiv:1306.6709v42013
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Timothy Yang, Galen Andrew, Hubert Eichner +5
cs.LGstat.MLarXiv:1812.02903v12018
Distilling a Neural Network Into a Soft Decision Tree
Nicholas Frosst, Geoffrey Hinton
cs.LGcs.AIstat.MLarXiv:1711.09784v12017
Domain Generalization for Object Recognition with Multi-task Autoencoders
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang +1
cs.CVcs.AIcs.LGarXiv:1508.07680v12015
Estimating Training Data Influence by Tracing Gradient Descent
Garima Pruthi, Frederick Liu, Mukund Sundararajan +1
cs.LGstat.MLarXiv:2002.08484v32020
Hopfield Networks is All You Need
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner +13
cs.NEcs.CLcs.LGarXiv:2008.02217v32020

Machine Learning (stat)

Hyper-Parameter Optimization: A Review of Algorithms and Applications

GPT-GNN: Generative Pre-Training of Graph Neural Networks

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Value Iteration Networks

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Discovering Symbolic Models from Deep Learning with Inductive Biases

Graph Representation Learning via Graphical Mutual Information Maximization

A Survey on Causal Inference

Machine Learning in IoT Security: Current Solutions and Future Challenges

BEHRT: Transformer for Electronic Health Records

Automated Vulnerability Detection in Source Code Using Deep Representation Learning

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

Structural Deep Clustering Network

Professor Forcing: A New Algorithm for Training Recurrent Networks

Scaling DoRA: High-Rank Adaptation via Factored Norms and Fused Kernels

Understanding over-squashing and bottlenecks on graphs via curvature

The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Explainable Prediction of Medical Codes from Clinical Text

Provably Efficient Reinforcement Learning with Linear Function Approximation

Deep learning with noisy labels: exploring techniques and remedies in medical image analysis

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Optimal Ratio for Data Splitting

Online Continual Learning with Maximally Interfered Retrieval

SuperSpike: Supervised learning in multi-layer spiking neural networks

Data Predictability Shapes Weibull Weight-Scale Growth in Transformer Training

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

Explainable Machine Learning in Deployment

ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Measuring Calibration in Deep Learning

SecureBoost: A Lossless Federated Learning Framework

Malware Detection by Eating a Whole EXE

Learning Deep Generative Models of Graphs

Applications of Deep Learning and Reinforcement Learning to Biological Data

The Computational Limits of Deep Learning

Lipschitz regularity of deep neural networks: analysis and efficient estimation

Contrastive learning of global and local features for medical image segmentation with limited annotations

Improving Diffusion Models for Inverse Problems using Manifold Constraints

A review of machine learning applications in wildfire science and management

Measuring Robustness to Natural Distribution Shifts in Image Classification

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

Certified Data Removal from Machine Learning Models

The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling

Improved denoising diffusion probabilistic models with efficient non-diagonal covariance modeling

A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

Artificial Entanglement in the Fine-Tuning of Large Language Models

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Variational Lossy Autoencoder

Learning from positive and unlabeled data: a survey

Simple Black-box Adversarial Attacks

A Survey on Metric Learning for Feature Vectors and Structured Data

Applied Federated Learning: Improving Google Keyboard Query Suggestions

Distilling a Neural Network Into a Soft Decision Tree

Domain Generalization for Object Recognition with Multi-task Autoencoders

Estimating Training Data Influence by Tracing Gradient Descent

Hopfield Networks is All You Need