Challenges in Markov chain Monte Carlo for Bayesian neural networks

15 October 2019

Papers citing "Challenges in Markov chain Monte Carlo for Bayesian neural networks"

20 / 20 papers shown

Title
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning Jiang Chang Deekshith Basvoju Aleksandar Vakanski Indrajit Charit Min Xian AI4CE 75 0 0 28 Jan 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin Peter Whalley Neil K. Chada Benedict Leimkuhler BDL 77 4 0 14 Oct 2024
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning Arsenii Ashukha Alexander Lyzhov Dmitry Molchanov Dmitry Vetrov UQCV FedML 75 318 0 15 Feb 2020
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape Johanni Brea Berfin Simsek Bernd Illing W. Gerstner 83 55 0 05 Jul 2019
$Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC$ Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC Aki Vehtari Andrew Gelman Daniel P. Simpson Bob Carpenter Paul-Christian Bürkner 37 926 0 19 Mar 2019
Exploring Weight Symmetry in Deep Neural Networks S. Hu Sergey Zagoruyko N. Komodakis 33 33 0 28 Dec 2018
Revisiting the Gelman-Rubin Diagnostic Dootika Vats Christina Knudson 52 136 0 21 Dec 2018
Minibatch Gibbs Sampling on Large Graphical Models Christopher De Sa Vincent Chen W. Wong 45 20 0 15 Jun 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 85 44 0 12 Jun 2018
Accelerating MCMC Algorithms Christian P. Robert Victor Elvira Nicholas G. Tawn Changye Wu 60 141 0 08 Apr 2018
The Expressive Power of Neural Networks: A View from the Width Zhou Lu Hongming Pu Feicheng Wang Zhiqiang Hu Liwei Wang 94 892 0 08 Sep 2017
Deep Learning: A Bayesian Perspective Nicholas G. Polson Vadim Sokolov BDL 75 117 0 01 Jun 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 52 598 0 13 Apr 2017
Merging MCMC Subposteriors through Gaussian-Process Approximations Christopher Nemeth Chris Sherlock 54 50 0 27 May 2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size F. Iandola Song Han Matthew W. Moskewicz Khalid Ashraf W. Dally Kurt Keutzer 137 7,465 0 24 Feb 2016
A Kernel Test of Goodness of Fit Kacper P. Chwialkowski Heiko Strathmann Arthur Gretton BDL 166 328 0 09 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 222 4,778 0 04 Jan 2016
Move Evaluation in Go Using Deep Convolutional Neural Networks Chris J. Maddison Aja Huang Ilya Sutskever David Silver FAtt 59 134 0 20 Dec 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 104 907 0 17 Feb 2014
Reversible Jump MCMC Simulated Annealing for Neural Networks Christophe Andrieu Nando de Freitas Arnaud Doucet BDL 51 48 0 16 Jan 2013