Nested Variational Compression in Deep Gaussian Processes

3 December 2014

Papers citing "Nested Variational Compression in Deep Gaussian Processes"

16 / 16 papers shown

Title
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 19 6 0 11 Dec 2021
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 27 54 0 30 Jun 2020
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 19 42 0 26 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 18 9 0 12 Oct 2019
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty Hao Zhou Yunyang Xiong Vikas Singh UQCV BDL 52 4 0 10 Jun 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 19 1,071 0 01 Nov 2017
Bayesian Alignments of Warped Multi-Output Gaussian Processes Markus Kaiser Clemens Otte Thomas Runkler Carl Henrik Ek 22 19 0 08 Oct 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni M. Deisenroth BDL GP 34 415 0 24 May 2017
Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar Edwin V. Bonilla Pietro Michiardi Maurizio Filippone BDL 14 142 0 14 Oct 2016
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis Andreas C. Damianou Neil D. Lawrence Carl Henrik Ek 14 12 0 17 Apr 2016
Inverse Reinforcement Learning via Deep Gaussian Process Ming Jin Andreas C. Damianou Pieter Abbeel C. Spanos OffRL BDL GP 18 22 0 26 Dec 2015
Variational Auto-encoded Deep Gaussian Processes Zhenwen Dai Andreas C. Damianou Javier I. González Neil D. Lawrence BDL 24 131 0 19 Nov 2015
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes A. G. Matthews J. Hensman Richard Turner Zoubin Ghahramani 30 189 0 27 Apr 2015
Early Stopping is Nonparametric Variational Inference D. Maclaurin David Duvenaud Ryan P. Adams BDL 30 95 0 06 Apr 2015
Gradient-based Hyperparameter Optimization through Reversible Learning D. Maclaurin David Duvenaud Ryan P. Adams DD 41 935 0 11 Feb 2015
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 92 271 0 24 Feb 2014