Variational Gaussian Processes
Variational Gaussian Processes is a variational inference framework that approximates the posterior distribution in Gaussian process models by optimizing a tractable variational distribution over latent variables, thereby combining the flexibility of Gaussian processes with scalable Bayesian inference.
88
Publications
5.2K
Citations
245
Authors
114
Institutions
Publications per year
2017–2026
47
88
Elastic theory of pinned flux lattices
Thierry Giamarchi, Pierre Le Doussal · Physical Review Letters · 1994 · 470 citations · Full text
GPflow: A Gaussian process library using TensorFlow
Alexander Matthews, Mark van der Wilk, Tom Nickson et al. · arXiv (Cornell University) · 2016 · 307 citations · Full text
The Variational Gaussian Approximation Revisited
Manfred Opper, Cédric Archambeau · Neural Computation · 2008 · 304 citations
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114
University of Cambridge
Cambridge, United Kingdom
9 h-index
21 publications
1.6K citations
University of Sheffield
Sheffield, United Kingdom
6 h-index
13 publications
1.4K citations
Georgia Institute of Technology
Atlanta, United States
4 h-index
7 publications
249 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
5 publications
103 citations
Ludwig-Maximilians-Universität München
Munich, Germany
3 h-index
4 publications
256 citations

Imperial College London
London, United Kingdom
2 h-index
3 publications
97 citations
Secretaría de Ciencia, Humanidades, Tecnología e Innovación
Mexico City, Mexico
2 h-index
2 publications
134 citations
Technische Universität Berlin
Berlin, Germany
2 h-index
2 publications
329 citations
1–8 of 114
Repository · 18 publications · 1.3K citations
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3 publications · 53 citations
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Conference · 3 publications · 93 citations
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2 publications · 112 citations
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2 publications · 400 citations
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