Deep Gaussian Processes for Regression using Approximate Expectation
Propagation

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

12 February 2016

Daniel Hernández-Lobato

José Miguel Hernández-Lobato

Papers citing "Deep Gaussian Processes for Regression using Approximate Expectation Propagation"

11 / 11 papers shown

Title
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation T. Bui Josiah Yan Richard Turner 58 25 0 23 May 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
Stochastic Expectation Propagation Yingzhen Li Jose Miguel Hernandez-Lobato Richard Turner 110 115 0 12 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 470 9,233 0 06 Jun 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
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 813 149,474 0 22 Dec 2014
Nested Variational Compression in Deep Gaussian Processes J. Hensman Neil D. Lawrence BDL 34 67 0 03 Dec 2014
Avoiding pathologies in very deep networks David Duvenaud Oren Rippel Ryan P. Adams Zoubin Ghahramani ODL BDL 79 158 0 24 Feb 2014
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 73 1,178 0 02 Nov 2012
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version M. Deisenroth S. Mohamed 51 45 0 12 Jul 2012