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Compositional uncertainty in deep Gaussian processes

Compositional uncertainty in deep Gaussian processes

Ivan Ustyuzhaninov
Ieva Kazlauskaite
Markus Kaiser
Erik Bodin
Neill D. F. Campbell
Carl Henrik Ek
    UQCV
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Papers citing "Compositional uncertainty in deep Gaussian processes"

6 / 6 papers shown
Title
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
S. Popescu
D. Sharp
James H. Cole
Konstantinos Kamnitsas
Ben Glocker
OOD
29
0
0
27 Jun 2022
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Chi-Ken Lu
Patrick Shafto
BDL
27
4
0
01 Oct 2021
Transforming Gaussian Processes With Normalizing Flows
Transforming Gaussian Processes With Normalizing Flows
Juan Maroñas
Oliver Hamelijnck
Jeremias Knoblauch
Theodoros Damoulas
34
34
0
03 Nov 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian W. Ober
Laurence Aitchison
BDL
26
60
0
17 May 2020
Doubly Sparse Variational Gaussian Processes
Doubly Sparse Variational Gaussian Processes
Vincent Adam
Stefanos Eleftheriadis
N. Durrande
A. Artemev
J. Hensman
27
24
0
15 Jan 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,167
0
06 Jun 2015