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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6,121 to 6,180 of 6,790

Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac +1
cs.LGq-bio.QMstat.MLarXiv:2203.17003v22022
Detecting Adversarial Samples from Artifacts
Reuben Feinman, Ryan R. Curtin, Saurabh Shintre +1
stat.MLcs.LGarXiv:1703.00410v32017
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro +2
cs.LGcs.AIstat.MEarXiv:2108.13264v42021
MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray +1
cs.LGcs.NEstat.MLarXiv:1502.03509v22015
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Shariq Iqbal, Fei Sha
cs.LGcs.AIcs.MAarXiv:1810.02912v22018
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani +2
cs.LGcs.DCmath.OCarXiv:1909.13014v42019
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo +5
cs.LGcs.CRstat.MLarXiv:1811.03728v12018
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma +3
cs.SIcs.LGstat.MLarXiv:1710.02971v42017
A trans-disciplinary review of deep learning research for water resources scientists
Chaopeng Shen
stat.MLcs.LGarXiv:1712.02162v32017
On Adaptive Attacks to Adversarial Example Defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel +1
cs.LGcs.CRstat.MLarXiv:2002.08347v22020
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst +1
stat.MLcs.LGarXiv:1612.07659v12016
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
stat.MLcs.LGarXiv:1706.02633v22017
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu, Jiashi Feng, Yudong Chen +3
stat.MLcs.LGarXiv:1804.03728v22018
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos +11
cs.LGstat.MLarXiv:1903.00374v52019
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta, Marin Bukov, Ching-Hao Wang +4
physics.comp-phcond-mat.stat-mechcs.LGarXiv:1803.08823v32018
Ladder Variational Autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe +2
stat.MLcs.LGarXiv:1602.02282v32016
Universal Transformers
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals +2
cs.CLcs.LGstat.MLarXiv:1807.03819v32018
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen +3
cs.LGstat.MLarXiv:2103.11251v22021
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
Nils Y. Hammerla, Shane Halloran, Thomas Ploetz
cs.LGcs.AIcs.HCarXiv:1604.08880v12016
Jukebox: A Generative Model for Music
Prafulla Dhariwal, Heewoo Jun, Christine Payne +3
eess.AScs.LGcs.SDarXiv:2005.00341v12020
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley Maddox, Timur Garipov, Pavel Izmailov +2
cs.LGcs.AIcs.CVarXiv:1902.02476v22019
Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily B. Fox, Carlos Guestrin
stat.MEcs.LGstat.MLarXiv:1402.4102v22014
On Fairness and Calibration
Geoff Pleiss, Manish Raghavan, Felix Wu +2
cs.LGcs.CYstat.MLarXiv:1709.02012v22017
Safety Verification of Deep Neural Networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang +1
cs.AIcs.LGstat.MLarXiv:1610.06940v32016
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors
Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan +3
cs.AIcs.LGstat.MLarXiv:1806.00064v12018
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade +1
cs.LGcs.AImath.OCarXiv:1909.04630v12019
Spectral Signatures in Backdoor Attacks
Brandon Tran, Jerry Li, Aleksander Madry
cs.LGcs.CRstat.MLarXiv:1811.00636v12018
A Survey on Knowledge Graph-Based Recommender Systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin +4
cs.IRcs.LGstat.MLarXiv:2003.00911v12020
On Evaluating Adversarial Robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot +6
cs.LGcs.CRstat.MLarXiv:1902.06705v22019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Kjersti Aas, Martin Jullum, Anders Løland
stat.MLcs.LGstat.MEarXiv:1903.10464v32019
Robust Aggregation for Federated Learning
Krishna Pillutla, Sham M. Kakade, Zaid Harchaoui
stat.MLcs.CRcs.LGarXiv:1912.13445v22019
Noisy Networks for Exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot +9
cs.LGstat.MLarXiv:1706.10295v32017
Inductive Representation Learning on Temporal Graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
cs.LGstat.MLarXiv:2002.07962v12020
On the Bottleneck of Graph Neural Networks and its Practical Implications
Uri Alon, Eran Yahav
cs.LGstat.MLarXiv:2006.05205v42020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson, Jonas Mueller, Alexander Shirkov +4
stat.MLcs.LGarXiv:2003.06505v12020
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
José Miguel Hernández-Lobato, Ryan P. Adams
stat.MLarXiv:1502.05336v22015
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes +3
cs.LGstat.MLarXiv:1812.05159v32018
Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig +1
stat.MLcs.AIcs.LGarXiv:1705.08551v32017
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler, Rob Fergus
cs.LGcs.NEstat.MLarXiv:1301.3557v12013
Spherical CNNs
Taco S. Cohen, Mario Geiger, Jonas Koehler +1
cs.LGstat.MLarXiv:1801.10130v32018
Toward Controlled Generation of Text
Zhiting Hu, Zichao Yang, Xiaodan Liang +2
cs.LGcs.AIcs.CLarXiv:1703.00955v42017
Distributional Reinforcement Learning with Quantile Regression
Will Dabney, Mark Rowland, Marc G. Bellemare +1
cs.AIcs.LGstat.MLarXiv:1710.10044v12017
Confident Learning: Estimating Uncertainty in Dataset Labels
Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang
stat.MLcs.LGarXiv:1911.00068v62019
Towards a Human-like Open-Domain Chatbot
Daniel Adiwardana, Minh-Thang Luong, David R. So +8
cs.CLcs.LGcs.NEarXiv:2001.09977v32020
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma +1
cs.LGcs.AIstat.MLarXiv:1908.01000v32019
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat +3
cs.LGstat.MLarXiv:2007.07314v22020
fastai: A Layered API for Deep Learning
Jeremy Howard, Sylvain Gugger
cs.LGcs.CVcs.NEarXiv:2002.04688v22020
On Detecting Adversarial Perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer +1
stat.MLcs.AIcs.CVarXiv:1702.04267v22017
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy +2
cs.LGstat.MLarXiv:1906.03671v22019
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
Tim Salimans, Andrej Karpathy, Xi Chen +1
cs.LGstat.MLarXiv:1701.05517v12017
Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim +2
cs.LGcs.CVcs.GRarXiv:2002.10099v22020
Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani +6
cs.LGstat.MLarXiv:1712.00409v12017
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Rex Ying +2
cs.LGcs.AIstat.MLarXiv:1806.02473v32018
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami +1
cs.LGstat.MLarXiv:1905.10497v22019
Recent Advances in Open Set Recognition: A Survey
Chuanxing Geng, Sheng-jun Huang, Songcan Chen
cs.LGstat.MLarXiv:1811.08581v42018
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Mehdi S. M. Sajjadi, Bernhard Schölkopf, Michael Hirsch
cs.CVcs.AIstat.MLarXiv:1612.07919v22016
Order Matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, Manjunath Kudlur
stat.MLcs.CLcs.LGarXiv:1511.06391v42015
Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
stat.MLcs.LGarXiv:1502.03492v32015
On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron van den Oord +2
cs.LGstat.MLarXiv:1905.06922v12019
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Haowen Xu, Wenxiao Chen, Nengwen Zhao +10
cs.LGstat.MLarXiv:1802.03903v12018

Machine Learning (stat)

Equivariant Diffusion for Molecule Generation in 3D

Detecting Adversarial Samples from Artifacts

Deep Reinforcement Learning at the Edge of the Statistical Precipice

MADE: Masked Autoencoder for Distribution Estimation

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

A trans-disciplinary review of deep learning research for water resources scientists

On Adaptive Attacks to Adversarial Example Defenses

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm

Model-Based Reinforcement Learning for Atari

A high-bias, low-variance introduction to Machine Learning for physicists

Ladder Variational Autoencoders

Universal Transformers

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables

Jukebox: A Generative Model for Music

A Simple Baseline for Bayesian Uncertainty in Deep Learning

Stochastic Gradient Hamiltonian Monte Carlo

On Fairness and Calibration

Safety Verification of Deep Neural Networks

Efficient Low-rank Multimodal Fusion with Modality-Specific Factors

Meta-Learning with Implicit Gradients

Spectral Signatures in Backdoor Attacks

A Survey on Knowledge Graph-Based Recommender Systems

On Evaluating Adversarial Robustness

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values

Robust Aggregation for Federated Learning

Noisy Networks for Exploration

Inductive Representation Learning on Temporal Graphs

On the Bottleneck of Graph Neural Networks and its Practical Implications

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

An Empirical Study of Example Forgetting during Deep Neural Network Learning

Safe Model-based Reinforcement Learning with Stability Guarantees

Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

Spherical CNNs

Toward Controlled Generation of Text

Distributional Reinforcement Learning with Quantile Regression

Confident Learning: Estimating Uncertainty in Dataset Labels

Towards a Human-like Open-Domain Chatbot

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Long-tail learning via logit adjustment

fastai: A Layered API for Deep Learning

On Detecting Adversarial Perturbations

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Implicit Geometric Regularization for Learning Shapes

Deep Learning Scaling is Predictable, Empirically

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Fair Resource Allocation in Federated Learning

Recent Advances in Open Set Recognition: A Survey

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Order Matters: Sequence to sequence for sets

Gradient-based Hyperparameter Optimization through Reversible Learning

On Variational Bounds of Mutual Information

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications