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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9,001 to 9,060 of 9,676

COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Xingyi Yang, Xuehai He, Jinyu Zhao +3
cs.LGcs.CVeess.IVarXiv:2003.13865v32020
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Mike Lewis, Yinhan Liu, Naman Goyal +5
cs.CLcs.LGstat.MLarXiv:1910.13461v12019
Summaries:한국어
Teaching Temporal Logics to Neural Networks
Christopher Hahn, Frederik Schmitt, Jens U. Kreber +2
cs.LOcs.AIcs.LGarXiv:2003.04218v32020
What is the State of Neural Network Pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle +1
cs.LGstat.MLarXiv:2003.03033v12020
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
Jiyang Gao, Chen Sun, Hang Zhao +4
cs.CVcs.LGstat.MLarXiv:2005.04259v12020
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
Jonathan A. Weyn, Dale R. Durran, Rich Caruana
physics.ao-phcs.LGstat.MLarXiv:2003.11927v12020
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
Husein Perez, Joseph H. M. Tah, Amir Mosavi
cs.CVcs.AIcs.LGarXiv:1908.04392v12019
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase +1
cs.LGcs.DCstat.MLarXiv:1910.02054v32019
Self-supervised ECG Representation Learning for Emotion Recognition
Pritam Sarkar, Ali Etemad
eess.SPcs.LGstat.MLarXiv:2002.03898v22020
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
Fabien Ringeval, Björn Schuller, Michel Valstar +14
cs.HCcs.CVcs.IRarXiv:1907.11510v12019
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays
Sivaramakrishnan Rajaraman, Jen Siegelman, Philip O. Alderson +3
eess.IVcs.CVcs.LGarXiv:2004.08379v32020
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Sebastian Raschka, Joshua Patterson, Corey Nolet
cs.LGstat.MLarXiv:2002.04803v22020
Pre-training Tasks for Embedding-based Large-scale Retrieval
Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang +2
cs.LGcs.CLcs.IRarXiv:2002.03932v12020
Convolutional Networks with Dense Connectivity
Gao Huang, Zhuang Liu, Geoff Pleiss +2
cs.LGcs.CVstat.MLarXiv:2001.02394v12020
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
stat.MLcond-mat.dis-nncs.LGarXiv:1806.01316v32018
NAS evaluation is frustratingly hard
Antoine Yang, Pedro M. Esperança, Fabio M. Carlucci
cs.LGcs.CVstat.MLarXiv:1912.12522v32019
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Sampo Kuutti, Richard Bowden, Yaochu Jin +2
cs.LGcs.CVeess.SYarXiv:1912.10773v12019
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, William Buchwalter
cs.LGstat.MLarXiv:1906.00910v22019
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Dimitrios Kollias, Panagiotis Tzirakis, Mihalis A. Nicolaou +5
cs.CVcs.AIcs.HCarXiv:1804.10938v52018
Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor, Aren Jansen, Ronnie Maor +7
eess.AScs.LGcs.SDarXiv:2002.12764v62020
Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, J. Zico Kolter
cs.LGstat.MLarXiv:2002.11569v22020
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu
cs.LGq-fin.CPstat.MLarXiv:1911.13288v12019
Large scale distributed neural network training through online distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos +3
cs.LGcs.AIstat.MLarXiv:1804.03235v22018
Machine Learning for Precipitation Nowcasting from Radar Images
Shreya Agrawal, Luke Barrington, Carla Bromberg +3
cs.CVcs.LGstat.MLarXiv:1912.12132v12019
Molecule Attention Transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha +3
cs.LGphysics.comp-phstat.MLarXiv:2002.08264v12020
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
Kacper Sokol, Peter Flach
cs.LGcs.AIstat.MLarXiv:2001.09734v12020
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
Szilárd Aradi
cs.LGeess.SYstat.MLarXiv:2001.11231v12020
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau +4
cs.CLcs.IRcs.LGarXiv:1911.00359v22019
Deep Learning for Spatio-Temporal Data Mining: A Survey
Senzhang Wang, Jiannong Cao, Philip S. Yu
cs.LGstat.MLarXiv:1906.04928v22019
Learning to Continually Learn
Shawn Beaulieu, Lapo Frati, Thomas Miconi +4
cs.LGcs.CVcs.NEarXiv:2002.09571v22020
Leveraging BERT for Extractive Text Summarization on Lectures
Derek Miller
cs.CLcs.LGcs.SDarXiv:1906.04165v12019
Machine Unlearning: Linear Filtration for Logit-based Classifiers
Thomas Baumhauer, Pascal Schöttle, Matthias Zeppelzauer
cs.LGstat.MLarXiv:2002.02730v22020
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff
physics.ao-phcs.AIphysics.data-anarXiv:1912.01752v22019
Accelerating Deep Learning by Focusing on the Biggest Losers
Angela H. Jiang, Daniel L. -K. Wong, Giulio Zhou +8
cs.LGstat.MLarXiv:1910.00762v12019
i-RevNet: Deep Invertible Networks
Jörn-Henrik Jacobsen, Arnold Smeulders, Edouard Oyallon
cs.LGcs.CVstat.MLarXiv:1802.07088v12018
Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang, Jia Deng
cs.LOcs.AIcs.LGarXiv:1905.09381v12019
State Representation Learning for Control: An Overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou +1
cs.AIcs.LGstat.MLarXiv:1802.04181v22018
FACE: Feasible and Actionable Counterfactual Explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez +2
cs.LGstat.MLarXiv:1909.09369v22019
Sparse Graph Attention Networks
Yang Ye, Shihao Ji
cs.LGstat.MLarXiv:1912.00552v22019
A Step Toward Quantifying Independently Reproducible Machine Learning Research
Edward Raff
cs.LGcs.AIcs.DLarXiv:1909.06674v12019
FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation
Le-Ha Hoang, Muhammad Abdullah Hanif, Muhammad Shafique
cs.LGstat.MLarXiv:1912.00941v12019
Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar +1
cs.LGcs.DCstat.MLarXiv:1908.07873v12019
Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses
Hamed Majidifard, Peng Jin, Yaw Adu-Gyamfi +1
cs.CVcs.LGstat.MLarXiv:1910.11123v22019
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Senthil Yogamani, Ciaran Hughes, Jonathan Horgan +14
cs.CVcs.AIcs.LGarXiv:1905.01489v32019
Natural Adversarial Examples
Dan Hendrycks, Kevin Zhao, Steven Basart +2
cs.LGcs.CVstat.MLarXiv:1907.07174v42019
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang +7
cs.LGcs.CVcs.ROarXiv:1903.11027v52019
Unsupervised Domain Adaptation through Self-Supervision
Yu Sun, Eric Tzeng, Trevor Darrell +1
cs.LGcs.CVstat.MLarXiv:1909.11825v22019
On Physical Adversarial Patches for Object Detection
Mark Lee, Zico Kolter
cs.CVcs.CRcs.LGarXiv:1906.11897v12019
Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction
Hanna Meyer, Christoph Reudenbach, Stephan Wöllauer +1
stat.APcs.LGstat.MLarXiv:1908.07805v12019
MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
Harsh Purohit, Ryo Tanabe, Kenji Ichige +4
cs.SDcs.LGeess.ASarXiv:1909.09347v12019
Speaker Anonymization Using X-vector and Neural Waveform Models
Fuming Fang, Xin Wang, Junichi Yamagishi +4
eess.AScs.CLcs.LGarXiv:1905.13561v12019
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Zonghan Wu, Shirui Pan, Guodong Long +2
cs.LGstat.MLarXiv:1906.00121v12019
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago +1
stat.MLcs.AIcs.LGarXiv:1905.11600v12019
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes +2
stat.MLcs.LGarXiv:1905.11001v52019
Survey on Deep Neural Networks in Speech and Vision Systems
Mahbubul Alam, Manar D. Samad, Lasitha Vidyaratne +2
cs.CVcs.LGcs.NEarXiv:1908.07656v22019
Using Natural Language for Reward Shaping in Reinforcement Learning
Prasoon Goyal, Scott Niekum, Raymond J. Mooney
cs.LGcs.AIstat.MLarXiv:1903.02020v22019
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan +1
cs.LGstat.MLarXiv:1904.06387v52019
Topic Modeling in Embedding Spaces
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
cs.IRcs.CLcs.LGarXiv:1907.04907v12019
Capsule Network Performance on Complex Data
Edgar Xi, Selina Bing, Yang Jin
stat.MLcs.LGarXiv:1712.03480v12017
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Esteban Real +3
cs.LGstat.MLarXiv:1902.09635v22019

Machine Learning (stat)

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Teaching Temporal Logics to Neural Networks

What is the State of Neural Network Pruning?

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Self-supervised ECG Representation Learning for Emotion Recognition

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Pre-training Tasks for Embedding-based Large-scale Retrieval

Convolutional Networks with Dense Connectivity

Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach

NAS evaluation is frustratingly hard

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Learning Representations by Maximizing Mutual Information Across Views

Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond

Towards Learning a Universal Non-Semantic Representation of Speech

Overfitting in adversarially robust deep learning

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Large scale distributed neural network training through online distillation

Machine Learning for Precipitation Nowcasting from Radar Images

Molecule Attention Transformer

One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

Deep Learning for Spatio-Temporal Data Mining: A Survey

Learning to Continually Learn

Leveraging BERT for Extractive Text Summarization on Lectures

Machine Unlearning: Linear Filtration for Logit-based Classifiers

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

Accelerating Deep Learning by Focusing on the Biggest Losers

i-RevNet: Deep Invertible Networks

Learning to Prove Theorems via Interacting with Proof Assistants

State Representation Learning for Control: An Overview

FACE: Feasible and Actionable Counterfactual Explanations

Sparse Graph Attention Networks

A Step Toward Quantifying Independently Reproducible Machine Learning Research

FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

Federated Learning: Challenges, Methods, and Future Directions

Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses

WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving

Natural Adversarial Examples

nuScenes: A multimodal dataset for autonomous driving

Unsupervised Domain Adaptation through Self-Supervision

On Physical Adversarial Patches for Object Detection

Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction

MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

Speaker Anonymization Using X-vector and Neural Waveform Models

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

Survey on Deep Neural Networks in Speech and Vision Systems

Using Natural Language for Reward Shaping in Reinforcement Learning

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

Topic Modeling in Embedding Spaces

Capsule Network Performance on Complex Data

NAS-Bench-101: Towards Reproducible Neural Architecture Search