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Doubly Stochastic Variational Inference for Deep Gaussian Processes
v1v2 (latest)

Doubly Stochastic Variational Inference for Deep Gaussian Processes

24 May 2017
Hugh Salimbeni
M. Deisenroth
    BDLGP
ArXiv (abs)PDFHTML

Papers citing "Doubly Stochastic Variational Inference for Deep Gaussian Processes"

50 / 238 papers shown
Nonnegative spatial factorization
Nonnegative spatial factorization
F. W. Townes
Barbara E. Engelhardt
147
12
0
12 Oct 2021
Probabilistic Metamodels for an Efficient Characterization of Complex
  Driving Scenarios
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
Max Winkelmann
Mike Kohlhoff
H. Tadjine
Steffen Müller
196
9
0
06 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Chi-Ken Lu
Patrick Shafto
BDL
259
4
0
01 Oct 2021
Non-stationary Gaussian process discriminant analysis with variable
  selection for high-dimensional functional data
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data
Weichang Yu
S. Wade
H. Bondell
Lamiae Azizi
141
6
0
29 Sep 2021
A theory of representation learning gives a deep generalisation of
  kernel methods
A theory of representation learning gives a deep generalisation of kernel methodsInternational Conference on Machine Learning (ICML), 2021
Adam X. Yang
Maxime Robeyns
Edward Milsom
Ben Anson
Nandi Schoots
Laurence Aitchison
BDL
557
14
0
30 Aug 2021
A variational approximate posterior for the deep Wishart process
A variational approximate posterior for the deep Wishart processNeural Information Processing Systems (NeurIPS), 2021
Sebastian W. Ober
Laurence Aitchison
BDL
163
11
0
21 Jul 2021
Subset-of-Data Variational Inference for Deep Gaussian-Processes
  Regression
Subset-of-Data Variational Inference for Deep Gaussian-Processes RegressionConference on Uncertainty in Artificial Intelligence (UAI), 2021
Ayush Jain
P. K. Srijith
Mohammad Emtiyaz Khan
BDLGP
165
0
0
17 Jul 2021
Input Dependent Sparse Gaussian Processes
Input Dependent Sparse Gaussian ProcessesInternational Conference on Machine Learning (ICML), 2021
B. Jafrasteh
Carlos Villacampa-Calvo
Daniel Hernández-Lobato
UQCV
193
7
0
15 Jul 2021
Deep Gaussian Process Emulation using Stochastic Imputation
Deep Gaussian Process Emulation using Stochastic Imputation
Deyu Ming
D. Williamson
S. Guillas
225
36
0
04 Jul 2021
Deep Gaussian Processes: A Survey
Deep Gaussian Processes: A Survey
Kalvik Jakkala
AI4CEGPBDL
158
25
0
21 Jun 2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice
  for Scalable Gaussian Processes
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian ProcessesInternational Conference on Machine Learning (ICML), 2021
Sanyam Kapoor
Marc Finzi
Ke Alexander Wang
A. Wilson
174
12
0
12 Jun 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 PerspectiveNeural Information Processing Systems (NeurIPS), 2021
Geoff Pleiss
John P. Cunningham
255
30
0
11 Jun 2021
Scalable Variational Gaussian Processes via Harmonic Kernel
  Decomposition
Scalable Variational Gaussian Processes via Harmonic Kernel DecompositionInternational Conference on Machine Learning (ICML), 2021
Shengyang Sun
Jiaxin Shi
A. Wilson
Roger C. Grosse
BDL
104
8
0
10 Jun 2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based
  Random Features
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesNeural Information Processing Systems (NeurIPS), 2021
Thomas M. McDonald
Mauricio A. Alvarez
225
10
0
10 Jun 2021
How to Evaluate Uncertainty Estimates in Machine Learning for
  Regression?
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?Neural Networks (NN), 2021
Laurens Sluijterman
Eric Cator
Tom Heskes
UQCV
271
38
0
07 Jun 2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output
  Pairs in Deep Learning
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep LearningNeural Information Processing Systems (NeurIPS), 2021
Jannik Kossen
Neil Band
Clare Lyle
Aidan Gomez
Tom Rainforth
Y. Gal
OOD3DPC
310
159
0
04 Jun 2021
Inferring Black Hole Properties from Astronomical Multivariate Time
  Series with Bayesian Attentive Neural Processes
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Ji Won Park
A. Villar
Yin Li
Yan-Fei Jiang
S. Ho
J. Lin
P. Marshall
A. Roodman
BDL
270
6
0
02 Jun 2021
Stochastic Collapsed Variational Inference for Structured Gaussian
  Process Regression Network
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
Rui Meng
Herbert Lee
K. Bouchard
181
2
0
01 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Sparse Uncertainty Representation in Deep Learning with Inducing WeightsNeural Information Processing Systems (NeurIPS), 2021
H. Ritter
Martin Kukla
Chen Zhang
Yingzhen Li
UQCVBDL
203
19
0
30 May 2021
Hierarchical Non-Stationary Temporal Gaussian Processes With
  $L^1$-Regularization
Hierarchical Non-Stationary Temporal Gaussian Processes With L1L^1L1-Regularization
Zheng Zhao
Rui Gao
Simo Särkkä
160
0
0
20 May 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A ReviewInternational Statistical Review (ISR), 2021
Vincent Fortuin
UQCVBDL
452
159
0
14 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Neural Networks as Point Estimates for Deep Gaussian ProcessesNeural Information Processing Systems (NeurIPS), 2021
Vincent Dutordoir
J. Hensman
Mark van der Wilk
Carl Henrik Ek
Zoubin Ghahramani
N. Durrande
BDLUQCV
314
33
0
10 May 2021
Exploring Uncertainty in Deep Learning for Construction of Prediction
  Intervals
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals
Yuandu Lai
Yucheng Shi
Yahong Han
Yunfeng Shao
Meiyu Qi
Bingshuai Li
UQCV
177
16
0
27 Apr 2021
Convolutional Normalizing Flows for Deep Gaussian Processes
Convolutional Normalizing Flows for Deep Gaussian ProcessesIEEE International Joint Conference on Neural Network (IJCNN), 2021
Haibin Yu
Dapeng Liu
Yizhou Chen
K. H. Low
Patrick Jaillet
BDL
202
6
0
17 Apr 2021
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model
  Inversion
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model InversionIsprs Journal of Photogrammetry and Remote Sensing (ISPRS J. Photogramm. Remote Sens.), 2020
D. Svendsen
Pablo Morales-Álvarez
A. Ruescas
Rafael Molina
Gustau Camps-Valls
206
31
0
16 Apr 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
216
29
0
12 Apr 2021
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator
  for Multi-fidelity Simulations
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations
Wei W. Xing
A. Shah
Peng Wang
Shandian Zhe
Robert M. Kirby
159
15
0
08 Apr 2021
Uncertainty-aware Remaining Useful Life predictor
Uncertainty-aware Remaining Useful Life predictor
Luca Biggio
Alexander Wieland
M. A. Chao
I. Kastanis
Olga Fink
AI4CE
135
8
0
08 Apr 2021
Accurate and Reliable Forecasting using Stochastic Differential
  Equations
Accurate and Reliable Forecasting using Stochastic Differential Equations
Peng Cui
Zhijie Deng
Wenbo Hu
Jun Zhu
UQCV
163
1
0
28 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning
The Promises and Pitfalls of Deep Kernel LearningConference on Uncertainty in Artificial Intelligence (UAI), 2021
Sebastian W. Ober
C. Rasmussen
Mark van der Wilk
UQCVBDL
388
119
0
24 Feb 2021
Using Gaussian Processes to Design Dynamic Experiments for Black-Box
  Model Discrimination under Uncertainty
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Simon Olofsson
Eduardo S. Schultz
A. Mhamdi
Alexander Mitsos
M. Deisenroth
Ruth Misener
140
0
0
07 Feb 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
1.2K
62
0
27 Dec 2020
Active Learning for Deep Gaussian Process Surrogates
Active Learning for Deep Gaussian Process Surrogates
Annie Sauer
R. Gramacy
D. Higdon
GPAI4CE
361
121
0
15 Dec 2020
Deep Gaussian Processes for geophysical parameter retrieval
Deep Gaussian Processes for geophysical parameter retrieval
D. Svendsen
Pablo Morales-Álvarez
Rafael Molina
Gustau Camps-Valls
GP
112
4
0
07 Dec 2020
Exploration in Online Advertising Systems with Deep Uncertainty-Aware
  Learning
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningKnowledge Discovery and Data Mining (KDD), 2020
Chao Du
Zhifeng Gao
Shuo Yuan
Lining Gao
Z. Li
Yifan Zeng
Xiaoqiang Zhu
Jian Xu
Kun Gai
Kuang-chih Lee
397
19
0
25 Nov 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques,
  Applications and Challenges
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and ChallengesInformation Fusion (Inf. Fusion), 2020
Moloud Abdar
Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
Tianpeng Liu
...
Xiaochun Cao
Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
BDLUQCV
959
2,296
0
12 Nov 2020
A Variational Infinite Mixture for Probabilistic Inverse Dynamics
  Learning
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
Hany Abdulsamad
Peter Nickl
Pascal Klink
Jan Peters
255
4
0
10 Nov 2020
Pathwise Conditioning of Gaussian Processes
Pathwise Conditioning of Gaussian Processes
James T. Wilson
Viacheslav Borovitskiy
Alexander Terenin
P. Mostowsky
M. Deisenroth
475
70
0
08 Nov 2020
Transforming Gaussian Processes With Normalizing Flows
Transforming Gaussian Processes With Normalizing Flows
Juan Maroñas
Oliver Hamelijnck
Jeremias Knoblauch
Theodoros Damoulas
403
36
0
03 Nov 2020
Sample-efficient reinforcement learning using deep Gaussian processes
Sample-efficient reinforcement learning using deep Gaussian processes
Charles W. L. Gadd
Markus Heinonen
Harri Lähdesmäki
Samuel Kaski
GPBDL
154
4
0
02 Nov 2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep
  Gaussian Processes
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian ProcessesInternational Conference on Machine Learning (ICML), 2020
Tim G. J. Rudner
Oscar Key
Y. Gal
Tom Rainforth
155
4
0
01 Nov 2020
Inter-domain Deep Gaussian Processes
Inter-domain Deep Gaussian ProcessesInternational Conference on Machine Learning (ICML), 2020
Tim G. J. Rudner
Dino Sejdinovic
Yarin Gal
277
12
0
01 Nov 2020
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
S. Popescu
D. Sharp
James H. Cole
Ben Glocker
307
5
0
28 Oct 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
  Data
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
Chacha Chen
Junjie Liang
Fenglong Ma
Lucas Glass
Jimeng Sun
Cao Xiao
163
29
0
22 Oct 2020
Probabilistic selection of inducing points in sparse Gaussian processes
Probabilistic selection of inducing points in sparse Gaussian processesConference on Uncertainty in Artificial Intelligence (UAI), 2020
Anders Kirk Uhrenholt
V. Charvet
B. S. Jensen
255
15
0
19 Oct 2020
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Characterizing Deep Gaussian Processes via Nonlinear Recurrence SystemsAAAI Conference on Artificial Intelligence (AAAI), 2020
Anh Tong
Jaesik Choi
373
2
0
19 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Sparse Spectrum Warped Input Measures for Nonstationary Kernel LearningNeural Information Processing Systems (NeurIPS), 2020
A. Tompkins
Rafael Oliveira
F. Ramos
224
6
0
09 Oct 2020
Using Bayesian deep learning approaches for uncertainty-aware building
  energy surrogate models
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models
Paul Westermann
R. Evins
AI4CE
82
53
0
05 Oct 2020
Deep kernel processes
Deep kernel processesInternational Conference on Machine Learning (ICML), 2020
Laurence Aitchison
Adam X. Yang
Sebastian W. Ober
BDL
362
42
0
04 Oct 2020
Stein Variational Gaussian Processes
Stein Variational Gaussian Processes
Thomas Pinder
Christopher Nemeth
David Leslie
BDL
252
7
0
25 Sep 2020
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