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Scalable Gaussian Process Variational Autoencoders

Scalable Gaussian Process Variational Autoencoders

26 October 2020
Metod Jazbec
Matthew Ashman
Vincent Fortuin
Michael Pearce
Stephan Mandt
Gunnar Rätsch
    DRL
    BDL
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Papers citing "Scalable Gaussian Process Variational Autoencoders"

27 / 27 papers shown
Title
Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
Xinxing Shi
Xiaoyu Jiang
Mauricio A. Álvarez
BDL
72
0
0
22 May 2025
Sparse Gaussian Neural Processes
Sparse Gaussian Neural Processes
Tommy Rochussen
Vincent Fortuin
BDL
UQCV
100
0
0
02 Apr 2025
On the Challenges and Opportunities in Generative AI
On the Challenges and Opportunities in Generative AI
Laura Manduchi
Kushagra Pandey
Robert Bamler
Ryan Cotterell
Sina Daubener
...
F. Wenzel
Frank Wood
Stephan Mandt
Vincent Fortuin
Vincent Fortuin
146
18
0
28 Feb 2024
Sparse Gaussian Process Variational Autoencoders
Sparse Gaussian Process Variational Autoencoders
Matthew Ashman
Jonathan So
Will Tebbutt
Vincent Fortuin
Michael Pearce
Richard Turner
76
35
0
20 Oct 2020
Quadruply Stochastic Gaussian Processes
Quadruply Stochastic Gaussian Processes
Trefor W. Evans
P. Nair
GP
19
3
0
04 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
236
10,591
0
17 Feb 2020
Mixture-of-Experts Variational Autoencoder for Clustering and Generating
  from Similarity-Based Representations on Single Cell Data
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
Andreas Kopf
Vincent Fortuin
Vignesh Ram Somnath
Manfred Claassen
DRL
33
12
0
17 Oct 2019
GP-VAE: Deep Probabilistic Time Series Imputation
GP-VAE: Deep Probabilistic Time Series Imputation
Vincent Fortuin
Dmitry Baranchuk
Gunnar Rätsch
Stephan Mandt
BDL
AI4TS
49
247
0
09 Jul 2019
An Introduction to Variational Autoencoders
An Introduction to Variational Autoencoders
Diederik P. Kingma
Max Welling
BDL
SSL
DRL
66
2,322
0
06 Jun 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression
Rates of Convergence for Sparse Variational Gaussian Process Regression
David R. Burt
C. Rasmussen
Mark van der Wilk
47
152
0
08 Mar 2019
Gaussian Process Prior Variational Autoencoders
Gaussian Process Prior Variational Autoencoders
F. P. Casale
Adrian Dalca
Luca Saglietti
Jennifer Listgarten
Nicolò Fusi
BDL
CML
51
135
0
28 Oct 2018
Taming VAEs
Taming VAEs
Danilo Jimenez Rezende
Fabio Viola
DRL
CML
41
183
0
01 Oct 2018
Scalable Generalized Dynamic Topic Models
Scalable Generalized Dynamic Topic Models
P. Jähnichen
F. Wenzel
Marius Kloft
Stephan Mandt
BDL
59
40
0
21 Mar 2018
Variational Message Passing with Structured Inference Networks
Variational Message Passing with Structured Inference Networks
Wu Lin
Nicolas Hubacher
Mohammad Emtiyaz Khan
BDL
52
54
0
15 Mar 2018
Disentangled Sequential Autoencoder
Disentangled Sequential Autoencoder
Yingzhen Li
Stephan Mandt
CoGe
69
270
0
08 Mar 2018
Inference Suboptimality in Variational Autoencoders
Inference Suboptimality in Variational Autoencoders
Chris Cremer
Xuechen Li
David Duvenaud
DRL
BDL
77
281
0
10 Jan 2018
Variational Inference for Gaussian Process Models with Linear Complexity
Variational Inference for Gaussian Process Models with Linear Complexity
Ching-An Cheng
Byron Boots
BDL
33
75
0
28 Nov 2017
Advances in Variational Inference
Advances in Variational Inference
Cheng Zhang
Judith Butepage
Hedvig Kjellström
Stephan Mandt
BDL
134
684
0
15 Nov 2017
Deep Unsupervised Clustering with Gaussian Mixture Variational
  Autoencoders
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul
P. Mediano
M. Garnelo
M. J. Lee
Hugh Salimbeni
Kai Arulkumaran
Murray Shanahan
DRL
54
651
0
08 Nov 2016
Improving Variational Inference with Inverse Autoregressive Flow
Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma
Tim Salimans
Rafal Jozefowicz
Xi Chen
Ilya Sutskever
Max Welling
BDL
DRL
88
1,805
0
15 Jun 2016
Composing graphical models with neural networks for structured
  representations and fast inference
Composing graphical models with neural networks for structured representations and fast inference
Matthew J. Johnson
David Duvenaud
Alexander B. Wiltschko
S. R. Datta
Ryan P. Adams
BDL
OCL
67
483
0
20 Mar 2016
Deep Kernel Learning
Deep Kernel Learning
A. Wilson
Zhiting Hu
Ruslan Salakhutdinov
Eric Xing
BDL
195
882
0
06 Nov 2015
Variational Inference with Normalizing Flows
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende
S. Mohamed
DRL
BDL
262
4,143
0
21 May 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
979
149,474
0
22 Dec 2014
Conditional Generative Adversarial Nets
Conditional Generative Adversarial Nets
M. Berk Mirza
Simon Osindero
GAN
SyDa
AI4CE
212
10,365
0
06 Nov 2014
Gaussian Processes for Big Data
Gaussian Processes for Big Data
J. Hensman
Nicolò Fusi
Neil D. Lawrence
GP
91
1,226
0
26 Sep 2013
Deep Gaussian Processes
Deep Gaussian Processes
Andreas C. Damianou
Neil D. Lawrence
GP
BDL
80
1,178
0
02 Nov 2012
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