Deterministic Variational Inference for Robust Bayesian Neural Networks

Deterministic Variational Inference for Robust Bayesian Neural Networks

9 October 2018

Sebastian Nowozin

José Miguel Hernández-Lobato

Alexander L. Gaunt

Papers citing "Deterministic Variational Inference for Robust Bayesian Neural Networks"

19 / 19 papers shown

Title
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 85 44 0 12 Jun 2018
Neural Control Variates for Variance Reduction Ruosi Wan Mingjun Zhong Haoyi Xiong Zhanxing Zhu BDL DRL 29 18 0 01 Jun 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 44 6 0 19 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 114 553 0 30 Apr 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 101 1,086 0 01 Nov 2017
Reducing Reparameterization Gradient Variance Andrew C. Miller N. Foti Alexander DÁmour Ryan P. Adams 47 84 0 22 May 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks Christos Louizos Max Welling BDL 129 456 0 06 Mar 2017
Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov BDL 90 825 0 19 Jan 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 468 5,748 0 05 Dec 2016
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors Christos Louizos Max Welling BDL 55 256 0 15 Mar 2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation T. Bui Daniel Hernández-Lobato Yingzhen Li José Miguel Hernández-Lobato Richard Turner BDL 65 235 0 12 Feb 2016
A Complete Recipe for Stochastic Gradient MCMC Yian Ma Tianqi Chen E. Fox BDL SyDa 47 484 0 15 Jun 2015
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 149 1,500 0 08 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 447 9,233 0 06 Jun 2015
Weight Uncertainty in Neural Networks Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra UQCV BDL 107 1,878 0 20 May 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 60 940 0 18 Feb 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 156 18,534 0 06 Feb 2015
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 69 902 0 17 Feb 2014
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring S. Ahn Anoop Korattikara Balan Max Welling 51 305 0 27 Jun 2012