Priors in Bayesian Deep Learning: A Review

14 May 2021

Papers citing "Priors in Bayesian Deep Learning: A Review"

29 / 179 papers shown

Title
Stochastic Variational Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 47 267 0 01 Nov 2016
Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar Edwin V. Bonilla Pietro Michiardi Maurizio Filippone BDL 39 144 0 14 Oct 2016
HyperNetworks David R Ha Andrew M. Dai Quoc V. Le 109 1,603 0 27 Sep 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 61 1,082 0 16 Aug 2016
Improving Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma Tim Salimans Rafal Jozefowicz Xi Chen Ilya Sutskever Max Welling BDL DRL 83 1,805 0 15 Jun 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 185 3,681 0 26 May 2016
Stick-Breaking Variational Autoencoders Marco Cote Padhraic Smyth BDL DRL 102 164 0 20 May 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 72 235 0 12 Feb 2016
Ladder Variational Autoencoders C. Sønderby T. Raiko Lars Maaløe Søren Kaae Sønderby Ole Winther BDL DRL 77 909 0 06 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 181 4,748 0 04 Jan 2016
Variational Auto-encoded Deep Gaussian Processes Zhenwen Dai Andreas C. Damianou Javier I. González Neil D. Lawrence BDL 40 131 0 19 Nov 2015
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 189 882 0 06 Nov 2015
Steps Toward Deep Kernel Methods from Infinite Neural Networks Tamir Hazan Tommi Jaakkola 39 83 0 20 Aug 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 258 4,143 0 21 May 2015
Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek Oren Rippel Kevin Swersky Ryan Kiros N. Satish N. Sundaram Md. Mostofa Ali Patwary P. Prabhat Ryan P. Adams BDL UQCV 70 1,041 0 19 Feb 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 64 940 0 18 Feb 2015
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It Peter Grünwald T. V. Ommen 69 265 0 11 Dec 2014
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 144 272 0 24 Feb 2014
Avoiding pathologies in very deep networks David Duvenaud Oren Rippel Ryan P. Adams Zoubin Ghahramani ODL BDL 79 158 0 24 Feb 2014
Student-t Processes as Alternatives to Gaussian Processes Amar Shah A. Wilson Zoubin Ghahramani GP 68 202 0 18 Feb 2014
Automatic Construction and Natural-Language Description of Nonparametric Regression Models J. Lloyd David Duvenaud Roger C. Grosse J. Tenenbaum Zoubin Ghahramani 54 241 0 18 Feb 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 367 16,962 0 20 Dec 2013
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 80 1,226 0 26 Sep 2013
Structure Discovery in Nonparametric Regression through Compositional Kernel Search David Duvenaud J. Lloyd Roger C. Grosse J. Tenenbaum Zoubin Ghahramani 53 509 0 20 Feb 2013
Gaussian Process Kernels for Pattern Discovery and Extrapolation A. Wilson Ryan P. Adams GP 53 604 0 18 Feb 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 75 1,178 0 02 Nov 2012
A Model of Inductive Bias Learning Jonathan Baxter 95 1,210 0 01 Jun 2011
Copula Processes A. Wilson Zoubin Ghahramani 126 113 0 07 Jun 2010