Probabilistic Backpropagation for Scalable Learning of Bayesian Neural
Networks

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

18 February 2015

José Miguel Hernández-Lobato

Papers citing "Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks"

10 / 109 papers shown

Title
Bayesian Semisupervised Learning with Deep Generative Models Jonathan Gordon José Miguel Hernández-Lobato BDL UQCV GAN 17 27 0 29 Jun 2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space José Miguel Hernández-Lobato James Requeima Edward O. Pyzer-Knapp Alán Aspuru-Guzik 17 177 0 06 Jun 2017
Dropout Inference in Bayesian Neural Networks with Alpha-divergences Yingzhen Li Y. Gal UQCV BDL 38 195 0 08 Mar 2017
Towards Bayesian Deep Learning: A Framework and Some Existing Methods Hao Wang Dit-Yan Yeung BDL 16 223 0 24 Aug 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 19 1,067 0 16 Aug 2016
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks Chunyuan Li Changyou Chen David Carlson Lawrence Carin ODL BDL 14 320 0 23 Dec 2015
Bayesian Dark Knowledge Masashi Sugiyama Vivek Rathod R. Garnett Max Welling BDL UQCV 20 257 0 14 Jun 2015
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks Eric T. Nalisnick Anima Anandkumar Padhraic Smyth 19 19 0 10 Jun 2015
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 27 1,489 0 08 Jun 2015
Early Stopping is Nonparametric Variational Inference D. Maclaurin D. Duvenaud Ryan P. Adams BDL 22 94 0 06 Apr 2015