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Deep convolutional Gaussian processes

Deep convolutional Gaussian processes

6 October 2018
Kenneth Blomqvist
Samuel Kaski
Markus Heinonen
    BDL
ArXivPDFHTML

Papers citing "Deep convolutional Gaussian processes"

16 / 16 papers shown
Title
Gaussian process deconvolution
Gaussian process deconvolution
Felipe A. Tobar
Arnaud Robert
Jorge F. Silva
33
5
0
08 May 2023
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
Geometry-Aware Hierarchical Bayesian Learning on Manifolds
Geometry-Aware Hierarchical Bayesian Learning on Manifolds
Yonghui Fan
Yalin Wang
18
2
0
30 Oct 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian
  Process Perspective
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
Priors in Bayesian Deep Learning: A Review
Vincent Fortuin
UQCV
BDL
31
124
0
14 May 2021
GPflux: A Library for Deep Gaussian Processes
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
35
23
0
12 Apr 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference
A Tutorial on Sparse Gaussian Processes and Variational Inference
Felix Leibfried
Vincent Dutordoir
S. T. John
N. Durrande
GP
42
49
0
27 Dec 2020
Pathwise Conditioning of Gaussian Processes
Pathwise Conditioning of Gaussian Processes
James T. Wilson
Viacheslav Borovitskiy
Alexander Terenin
P. Mostowsky
M. Deisenroth
18
58
0
08 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
A Framework for Interdomain and Multioutput Gaussian Processes
A Framework for Interdomain and Multioutput Gaussian Processes
Mark van der Wilk
Vincent Dutordoir
S. T. John
A. Artemev
Vincent Adam
J. Hensman
40
94
0
02 Mar 2020
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch
  Detection in LIGO
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Pablo Morales-Álvarez
Pablo Ruiz
S. Coughlin
Rafael Molina
Aggelos K. Katsaggelos
21
14
0
05 Nov 2019
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any
  Architecture are Gaussian Processes
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang
33
193
0
28 Oct 2019
On Exact Computation with an Infinitely Wide Neural Net
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora
S. Du
Wei Hu
Zhiyuan Li
Ruslan Salakhutdinov
Ruosong Wang
44
905
0
26 Apr 2019
Bayesian Deep Convolutional Networks with Many Channels are Gaussian
  Processes
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
Roman Novak
Lechao Xiao
Jaehoon Lee
Yasaman Bahri
Greg Yang
Jiri Hron
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
25
306
0
11 Oct 2018
Posterior Inference for Sparse Hierarchical Non-stationary Models
Posterior Inference for Sparse Hierarchical Non-stationary Models
K. Monterrubio-Gómez
L. Roininen
S. Wade
Theo Damoulas
Mark Girolami
27
27
0
04 Apr 2018
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
285
9,145
0
06 Jun 2015
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