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Structured Stochastic Gradient MCMC

19 July 2021

Papers citing "Structured Stochastic Gradient MCMC"

38 / 38 papers shown

Title
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 165 2 0 31 May 2024
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling Gianluigi Silvestri Emily Fertig David A. Moore L. Ambrogioni BDL TPM AI4CE 74 4 0 12 Oct 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 71 17 0 30 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 77 387 0 29 Apr 2021
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 88 632 0 14 Jul 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 132 656 0 20 Feb 2020
Automatic structured variational inference L. Ambrogioni Kate Lin Emily Fertig Sharad Vikram Max Hinne Dave Moore Marcel van Gerven BDL 75 31 0 03 Feb 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 213 1,718 0 05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 568 42,677 0 03 Dec 2019
Partitioned integrators for thermodynamic parameterization of neural networks Benedict Leimkuhler Charles Matthews Tiffany J. Vlaar ODL 27 22 0 30 Aug 2019
Bayesian Inference for Large Scale Image Classification Jonathan Heek Nal Kalchbrenner UQCV BDL 130 35 0 09 Aug 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks L. Cardelli Marta Kwiatkowska Luca Laurenti Nicola Paoletti A. Patané Matthew Wicker AAML 89 54 0 05 Mar 2019
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning Ruqi Zhang Chunyuan Li Jianyi Zhang Changyou Chen A. Wilson BDL 78 279 0 11 Feb 2019
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 107 44 0 12 Jun 2018
TensorFlow Distributions Joshua V. Dillon I. Langmore Dustin Tran E. Brevdo Srinivas Vasudevan David A. Moore Brian Patton Alexander A. Alemi Matt Hoffman Rif A. Saurous GP 110 352 0 28 Nov 2017
Generalizing Hamiltonian Monte Carlo with Neural Networks Daniel Levy Matthew D. Hoffman Jascha Narain Sohl-Dickstein BDL 75 130 0 25 Nov 2017
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 199 696 0 15 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 285 8,928 0 25 Aug 2017
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI Justin Domke BDL 68 6 0 20 Jun 2017
Stochastic Gradient MCMC Methods for Hidden Markov Models Yian Ma N. Foti E. Fox BDL 43 32 0 14 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,877 0 14 Jun 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 67 599 0 13 Apr 2017
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 98 1,094 0 16 Aug 2016
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 435 18,361 0 27 May 2016
A Variational Analysis of Stochastic Gradient Algorithms Stephan Mandt Matthew D. Hoffman David M. Blei 68 161 0 08 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 307 4,817 0 04 Jan 2016
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks Chunyuan Li Changyou Chen David Carlson Lawrence Carin ODL BDL 102 327 0 23 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.3K 194,641 0 10 Dec 2015
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan Giordano Tamara Broderick Michael I. Jordan 76 84 0 12 Jun 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 322 4,198 0 21 May 2015
Early Stopping is Nonparametric Variational Inference D. Maclaurin David Duvenaud Ryan P. Adams BDL 92 95 0 06 Apr 2015
Structured Stochastic Variational Inference Matthew D. Hoffman David M. Blei BDL 97 87 0 16 Apr 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 128 913 0 17 Feb 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 153 1,167 0 31 Dec 2013
Variational MCMC Nando de Freitas Pedro A. d. F. R. Højen-Sørensen Michael I. Jordan Stuart J. Russell BDL 101 105 0 10 Jan 2013
Variational Inference in Nonconjugate Models Chong-Jun Wang David M. Blei BDL 133 228 0 19 Sep 2012
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 277 2,628 0 29 Jun 2012
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring S. Ahn Anoop Korattikara Balan Max Welling 79 306 0 27 Jun 2012