Variational Auto-encoded Deep Gaussian Processes

19 November 2015

Papers citing "Variational Auto-encoded Deep Gaussian Processes"

21 / 21 papers shown

Title
Residual Deep Gaussian Processes on Manifolds Kacper Wyrwal Andreas Krause Viacheslav Borovitskiy BDL 49 0 0 31 Oct 2024
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 13 6 0 11 Dec 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 17 19 0 21 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
GPflux: A Library for Deep Gaussian Processes Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried A. Artemev Mark van der Wilk J. Hensman M. Deisenroth S. T. John GP 33 23 0 12 Apr 2021
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference Hao Zhang Bo Chen Yulai Cong D. Guo Hongwei Liu Mingyuan Zhou BDL 24 27 0 15 Jun 2020
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 16 42 0 26 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 16 9 0 12 Oct 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak J. Gardner UQCV BDL 27 3 0 31 May 2019
Efficient Global Optimization using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab 11 19 0 11 Sep 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 20 85 0 24 Mar 2018
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 38 684 0 15 Nov 2017
Auto-Differentiating Linear Algebra Matthias Seeger A. Hetzel Zhenwen Dai Eric Meissner Neil D. Lawrence 17 38 0 24 Oct 2017
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis Tom Nicholson M. Deisenroth J. Hensman 24 111 0 30 May 2017
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models K. Krauth Edwin V. Bonilla Kurt Cutajar Maurizio Filippone GP BDL 14 54 0 18 Oct 2016
Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar Edwin V. Bonilla Pietro Michiardi Maurizio Filippone BDL 14 141 0 14 Oct 2016
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units Stefanos Eleftheriadis Ognjen Rudovic M. Deisenroth M. Pantic DRL 25 28 0 16 Aug 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 86 271 0 24 Feb 2014
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,634 0 03 Jul 2012