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)

6,541 to 6,600 of 6,780

Deep Interest Network for Click-Through Rate Prediction
Guorui Zhou, Chengru Song, Xiaoqiang Zhu +7
stat.MLcs.LGarXiv:1706.06978v42017
Linformer: Self-Attention with Linear Complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa +2
cs.LGstat.MLarXiv:2006.04768v32020
Link Prediction Based on Graph Neural Networks
Muhan Zhang, Yixin Chen
cs.LGstat.MLarXiv:1802.09691v32018
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende +2
stat.MLcs.LGarXiv:1912.02762v22019
When Does Label Smoothing Help?
Rafael Müller, Simon Kornblith, Geoffrey Hinton
cs.LGstat.MLarXiv:1906.02629v32019
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Wonjae Kim, Bokyung Son, Ildoo Kim
stat.MLcs.LGarXiv:2102.03334v22021
Visualizing the Loss Landscape of Neural Nets
Hao Li, Zheng Xu, Gavin Taylor +2
cs.LGcs.CVstat.MLarXiv:1712.09913v32017
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian +3
cs.LGcs.AIcs.CVarXiv:1806.03536v22018
Generating Diverse High-Fidelity Images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, Oriol Vinyals
cs.LGcs.CVstat.MLarXiv:1906.00446v12019
A Tutorial on Bayesian Optimization
Peter I. Frazier
stat.MLcs.LGmath.OCarXiv:1807.02811v12018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer +4
stat.MLarXiv:1711.11279v52017
Deep Reinforcement Learning that Matters
Peter Henderson, Riashat Islam, Philip Bachman +3
cs.LGstat.MLarXiv:1709.06560v32017
Group Equivariant Convolutional Networks
Taco S. Cohen, Max Welling
cs.LGstat.MLarXiv:1602.07576v32016
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Dan Hendrycks, Steven Basart, Norman Mu +10
cs.CVcs.LGstat.MLarXiv:2006.16241v32020
Learning Structured Sparsity in Deep Neural Networks
Wei Wen, Chunpeng Wu, Yandan Wang +2
cs.NEcs.LGstat.MLarXiv:1608.03665v42016
Listen, Attend and Spell
William Chan, Navdeep Jaitly, Quoc V. Le +1
cs.CLcs.LGcs.NEarXiv:1508.01211v22015
Deeply-Supervised Nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher +2
stat.MLcs.CVcs.LGarXiv:1409.5185v22014
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee +1
cs.LGq-bio.NCstat.MLarXiv:1905.00414v42019
Mixed Precision Training
Paulius Micikevicius, Sharan Narang, Jonah Alben +8
cs.AIcs.LGstat.MLarXiv:1710.03740v32017
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce, Matthias Hein
cs.LGcs.CVstat.MLarXiv:2003.01690v22020
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
stat.APcs.CYstat.MLarXiv:1610.07524v12016
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho +2
cs.LGcs.NEstat.MLarXiv:1606.01865v22016
Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky +2
cs.LGcs.CVstat.MLarXiv:2006.10029v22020
Certified Adversarial Robustness via Randomized Smoothing
Jeremy M Cohen, Elan Rosenfeld, J. Zico Kolter
cs.LGstat.MLarXiv:1902.02918v22019
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang, Phillip Isola
cs.LGcs.CVstat.MLarXiv:2005.10242v102020
Methods for Interpreting and Understanding Deep Neural Networks
Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
cs.LGstat.MLarXiv:1706.07979v12017
On the Spectral Bias of Neural Networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit +5
stat.MLcs.LGarXiv:1806.08734v32018
Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying, Jiaxuan You, Christopher Morris +3
cs.LGcs.NEcs.SIarXiv:1806.08804v42018
A Comparative Analysis of XGBoost
Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz
cs.LGstat.MLarXiv:1911.01914v12019
Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly +3
cs.CVcs.LGstat.MLarXiv:1810.03292v32018
DGM: A deep learning algorithm for solving partial differential equations
Justin Sirignano, Konstantinos Spiliopoulos
q-fin.MFmath.NAq-fin.CParXiv:1708.07469v52017
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
cs.CLcs.AIcs.CYarXiv:2206.04615v32022
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, Sakurai Kouichi
cs.LGcs.CVstat.MLarXiv:1710.08864v72017
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5
cs.LGphysics.chem-phstat.MLarXiv:1703.00564v32017
Generating Long Sequences with Sparse Transformers
Rewon Child, Scott Gray, Alec Radford +1
cs.LGstat.MLarXiv:1904.10509v12019
Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu +5
cs.CLcs.LGstat.MLarXiv:1909.08593v22019
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert +9
cs.LGstat.MLarXiv:1911.08265v22019
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu +5
cs.LGstat.MLarXiv:1804.06872v32018
Multimodal Unsupervised Image-to-Image Translation
Xun Huang, Ming-Yu Liu, Serge Belongie +1
cs.CVcs.LGstat.MLarXiv:1804.04732v22018
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine, Aviral Kumar, George Tucker +1
cs.LGcs.AIstat.MLarXiv:2005.01643v32020
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub M. Tomczak, Max Welling
cs.LGstat.MLarXiv:1802.04712v42018
Online Learning for Matrix Factorization and Sparse Coding
Julien Mairal, Francis Bach, Jean Ponce +1
stat.MLcs.LGmath.OCarXiv:0908.0050v22009
SmoothGrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim +2
cs.LGcs.CVstat.MLarXiv:1706.03825v12017
Computational Optimal Transport
Gabriel Peyré, Marco Cuturi
stat.MLarXiv:1803.00567v42018
Unsupervised Data Augmentation for Consistency Training
Qizhe Xie, Zihang Dai, Eduard Hovy +2
cs.LGcs.AIcs.CLarXiv:1904.12848v62019
A Tutorial on Principal Component Analysis
Jonathon Shlens
cs.LGstat.MLarXiv:1404.1100v12014
Meta-Learning in Neural Networks: A Survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli +1
cs.LGstat.MLarXiv:2004.05439v22020
Attention-Based Models for Speech Recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk +2
cs.CLcs.LGcs.NEarXiv:1506.07503v12015
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon +4
physics.data-anmath.OCstat.AParXiv:1202.6294v22012
Deep Transfer Learning with Joint Adaptation Networks
Mingsheng Long, Han Zhu, Jianmin Wang +1
cs.LGstat.MLarXiv:1605.06636v22016
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Xuezhe Ma, Eduard Hovy
cs.LGcs.CLstat.MLarXiv:1603.01354v52016
Self-training with Noisy Student improves ImageNet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy +1
cs.LGcs.CVstat.MLarXiv:1911.04252v42019
Quantum Machine Learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti +3
quant-phcond-mat.str-elstat.MLarXiv:1611.09347v22016
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi +1
cs.CVcs.AIcs.LGarXiv:1610.08401v32016
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed +1
cs.LGstat.MLarXiv:1406.5298v22014
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie +1
cs.LGcs.CLstat.MLarXiv:1902.10197v12019
Stochastic Variational Inference
Matt Hoffman, David M. Blei, Chong Wang +1
stat.MLcs.AIstat.COarXiv:1206.7051v32012
A Survey on Deep Transfer Learning
Chuanqi Tan, Fuchun Sun, Tao Kong +3
cs.LGstat.MLarXiv:1808.01974v12018
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Han Zhang, Tao Xu, Hongsheng Li +4
cs.CVcs.AIstat.MLarXiv:1612.03242v22016
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
Ling Zhao, Yujiao Song, Chao Zhang +5
cs.LGstat.MLarXiv:1811.05320v32018

Machine Learning (stat)

Deep Interest Network for Click-Through Rate Prediction

Linformer: Self-Attention with Linear Complexity

Link Prediction Based on Graph Neural Networks

Normalizing Flows for Probabilistic Modeling and Inference

When Does Label Smoothing Help?

ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Visualizing the Loss Landscape of Neural Nets

Representation Learning on Graphs with Jumping Knowledge Networks

Generating Diverse High-Fidelity Images with VQ-VAE-2

A Tutorial on Bayesian Optimization

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Deep Reinforcement Learning that Matters

Group Equivariant Convolutional Networks

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Learning Structured Sparsity in Deep Neural Networks

Listen, Attend and Spell

Deeply-Supervised Nets

Similarity of Neural Network Representations Revisited

Mixed Precision Training

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Big Self-Supervised Models are Strong Semi-Supervised Learners

Certified Adversarial Robustness via Randomized Smoothing

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Methods for Interpreting and Understanding Deep Neural Networks

On the Spectral Bias of Neural Networks

Hierarchical Graph Representation Learning with Differentiable Pooling

A Comparative Analysis of XGBoost

Sanity Checks for Saliency Maps

DGM: A deep learning algorithm for solving partial differential equations

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

One pixel attack for fooling deep neural networks

MoleculeNet: A Benchmark for Molecular Machine Learning

Generating Long Sequences with Sparse Transformers

Fine-Tuning Language Models from Human Preferences

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Multimodal Unsupervised Image-to-Image Translation

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Attention-based Deep Multiple Instance Learning

Online Learning for Matrix Factorization and Sparse Coding

SmoothGrad: removing noise by adding noise

Computational Optimal Transport

Unsupervised Data Augmentation for Consistency Training

A Tutorial on Principal Component Analysis

Meta-Learning in Neural Networks: A Survey

Attention-Based Models for Speech Recognition

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Deep Transfer Learning with Joint Adaptation Networks

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Self-training with Noisy Student improves ImageNet classification

Quantum Machine Learning

Universal adversarial perturbations

Semi-Supervised Learning with Deep Generative Models

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Stochastic Variational Inference

A Survey on Deep Transfer Learning

StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction