Stochastic Variational Deep Kernel Learning

1 November 2016

Papers citing "Stochastic Variational Deep Kernel Learning"

21 / 21 papers shown

Title
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise T. Pouplin Alan Jeffares Nabeel Seedat Mihaela van der Schaar 244 3 0 05 Jun 2024
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu Zi Lin Shreyas Padhy Dustin Tran Tania Bedrax-Weiss Balaji Lakshminarayanan UQCV BDL 110 444 0 17 Jun 2020
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang 80 197 0 28 Oct 2019
The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables L. Ambrogioni Umut Güçlü Marcel van Gerven E. Maris BDL 111 47 0 19 May 2017
Variational Auto-encoded Deep Gaussian Processes Zhenwen Dai Andreas C. Damianou Javier I. González Neil D. Lawrence BDL 45 131 0 19 Nov 2015
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 189 882 0 06 Nov 2015
Thoughts on Massively Scalable Gaussian Processes A. Wilson Christoph Dann H. Nickisch 71 110 0 05 Nov 2015
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes T. Nickson Tom Gunter C. Lloyd Michael A. Osborne Stephen J. Roberts 33 21 0 27 Oct 2015
MCMC for Variationally Sparse Gaussian Processes J. Hensman A. G. Matthews Maurizio Filippone Zoubin Ghahramani 49 141 0 12 Jun 2015
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) A. Wilson H. Nickisch GP 59 512 0 03 Mar 2015
A la Carte - Learning Fast Kernels Zichao Yang Alex Smola Le Song A. Wilson 42 132 0 19 Dec 2014
Scalable Variational Gaussian Process Classification J. Hensman A. G. Matthews Zoubin Ghahramani BDL 49 641 0 07 Nov 2014
Caffe: Convolutional Architecture for Fast Feature Embedding Yangqing Jia Evan Shelhamer Jeff Donahue Sergey Karayev Jonathan Long Ross B. Girshick S. Guadarrama Trevor Darrell VLM BDL 3DV 206 14,703 0 20 Jun 2014
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 144 272 0 24 Feb 2014
Automatic Construction and Natural-Language Description of Nonparametric Regression Models J. Lloyd David Duvenaud Roger C. Grosse J. Tenenbaum Zoubin Ghahramani 57 241 0 18 Feb 2014
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Y. Gal Mark van der Wilk C. Rasmussen 60 150 0 06 Feb 2014
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 89 1,226 0 26 Sep 2013
Gaussian Process Kernels for Pattern Discovery and Extrapolation A. Wilson Ryan P. Adams GP 58 604 0 18 Feb 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 78 1,178 0 02 Nov 2012
Gaussian Process Regression Networks A. Wilson David A. Knowles Zoubin Ghahramani GP BDL 122 192 0 19 Oct 2011
Additive Kernels for Gaussian Process Modeling N. Durrande D. Ginsbourger O. Roustant 64 25 0 21 Mar 2011