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Functional Variational Bayesian Neural Networks

Functional Variational Bayesian Neural Networks

14 March 2019
Shengyang Sun
Guodong Zhang
Jiaxin Shi
Roger C. Grosse
    BDL
ArXiv (abs)PDFHTML

Papers citing "Functional Variational Bayesian Neural Networks"

50 / 85 papers shown
Title
Revisiting Unbiased Implicit Variational Inference
Revisiting Unbiased Implicit Variational Inference
Tobias Pielok
Bernd Bischl
David Rügamer
BDL
83
0
0
04 Jun 2025
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
Shireen Kudukkil Manchingal
Andrew Bradley
Julian F. P. Kooij
Keivan K1 Shariatmadar
Neil Yorke-Smith
Fabio Cuzzolin
165
1
0
08 May 2025
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference
Jasmeet Kaur
BDLUQCV
124
0
0
15 Mar 2025
Bayesian Computation in Deep Learning
Bayesian Computation in Deep Learning
Wenlong Chen
Bolian Li
Ruqi Zhang
Yingzhen Li
BDL
114
0
0
25 Feb 2025
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
Tristan Cinquin
Robert Bamler
UQCVBDL
132
2
0
06 Jun 2024
Continual Learning via Sequential Function-Space Variational Inference
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner
Freddie Bickford-Smith
Qixuan Feng
Yee Whye Teh
Y. Gal
74
42
0
28 Dec 2023
Density Uncertainty Layers for Reliable Uncertainty Estimation
Density Uncertainty Layers for Reliable Uncertainty Estimation
Yookoon Park
David M. Blei
UQCVBDL
58
2
0
21 Jun 2023
Guided Deep Kernel Learning
Guided Deep Kernel Learning
Idan Achituve
Gal Chechik
Ethan Fetaya
BDL
66
7
0
19 Feb 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Ethan Goan
Dimitri Perrin
Kerrie Mengersen
Clinton Fookes
67
0
0
17 Feb 2023
Diffusion Generative Models in Infinite Dimensions
Diffusion Generative Models in Infinite Dimensions
Gavin Kerrigan
Justin Ley
Padhraic Smyth
DiffM
114
35
0
01 Dec 2022
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma
Sebastian Farquhar
Eric T. Nalisnick
Tom Rainforth
BDL
84
57
0
11 Nov 2022
Amortized Variational Inference: A Systematic Review
Amortized Variational Inference: A Systematic Review
Ankush Ganguly
Sanjana Jain
Ukrit Watchareeruetai
86
17
0
22 Sep 2022
On the detrimental effect of invariances in the likelihood for
  variational inference
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle
R. Herbrich
Tim Januschowski
Bernie Wang
Jan Gasthaus
67
10
0
15 Sep 2022
Correcting Model Bias with Sparse Implicit Processes
Correcting Model Bias with Sparse Implicit Processes
Simón Rodríguez Santana
Luis A. Ortega Andrés
Daniel Hernández-Lobato
B. Zaldívar
BDL
51
1
0
21 Jul 2022
Deep Learning and Symbolic Regression for Discovering Parametric
  Equations
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang
Samuel Kim
Peter Y. Lu
M. Soljavcić
72
21
0
01 Jul 2022
Tackling covariate shift with node-based Bayesian neural networks
Tackling covariate shift with node-based Bayesian neural networks
Trung Trinh
Markus Heinonen
Luigi Acerbi
Samuel Kaski
BDLUQCV
63
6
0
06 Jun 2022
Deep Ensemble as a Gaussian Process Approximate Posterior
Deep Ensemble as a Gaussian Process Approximate Posterior
Zhijie Deng
Feng Zhou
Jianfei Chen
Guoqiang Wu
Jun Zhu
UQCV
41
5
0
30 Apr 2022
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series
Xing Han
Jing Hu
Joydeep Ghosh
AI4TS
78
2
0
22 Dec 2021
Periodic Activation Functions Induce Stationarity
Periodic Activation Functions Induce Stationarity
Lassi Meronen
Martin Trapp
Arno Solin
BDL
91
21
0
26 Oct 2021
Variational Bayesian Approximation of Inverse Problems using Sparse
  Precision Matrices
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices
Jan Povala
Ieva Kazlauskaite
Eky Febrianto
F. Cirak
Mark Girolami
91
23
0
22 Oct 2021
The Neural Testbed: Evaluating Joint Predictions
The Neural Testbed: Evaluating Joint Predictions
Ian Osband
Zheng Wen
S. Asghari
Vikranth Dwaracherla
Botao Hao
M. Ibrahimi
Dieterich Lawson
Xiuyuan Lu
Brendan O'Donoghue
Benjamin Van Roy
UQCV
94
22
0
09 Oct 2021
Training on Test Data with Bayesian Adaptation for Covariate Shift
Training on Test Data with Bayesian Adaptation for Covariate Shift
Aurick Zhou
Sergey Levine
OODTTA
112
13
0
27 Sep 2021
Pre-trained Gaussian processes for Bayesian optimization
Pre-trained Gaussian processes for Bayesian optimization
Zehao Wang
George E. Dahl
Kevin Swersky
Chansoo Lee
Zachary Nado
Justin Gilmer
Jasper Snoek
Zoubin Ghahramani
151
46
0
16 Sep 2021
A variational approximate posterior for the deep Wishart process
A variational approximate posterior for the deep Wishart process
Sebastian W. Ober
Laurence Aitchison
BDL
42
11
0
21 Jul 2021
A Survey of Uncertainty in Deep Neural Networks
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDLUQCVOOD
242
1,177
0
07 Jul 2021
Repulsive Deep Ensembles are Bayesian
Repulsive Deep Ensembles are Bayesian
Francesco DÁngelo
Vincent Fortuin
UQCVBDL
129
101
0
22 Jun 2021
Model Selection for Bayesian Autoencoders
Model Selection for Bayesian Autoencoders
Ba-Hien Tran
Simone Rossi
Dimitrios Milios
Pietro Michiardi
Edwin V. Bonilla
Maurizio Filippone
BDL
82
13
0
11 Jun 2021
Meta-Learning Reliable Priors in the Function Space
Meta-Learning Reliable Priors in the Function Space
Jonas Rothfuss
Dominique Heyn
Jinfan Chen
Andreas Krause
84
28
0
06 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
H. Ritter
Martin Kukla
Chen Zhang
Yingzhen Li
UQCVBDL
90
17
0
30 May 2021
Can we imitate the principal investor's behavior to learn option price?
Can we imitate the principal investor's behavior to learn option price?
Xin Jin
49
0
0
24 May 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A Review
Vincent Fortuin
UQCVBDL
137
134
0
14 May 2021
Deep Learning for Bayesian Optimization of Scientific Problems with
  High-Dimensional Structure
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim
Peter Y. Lu
Charlotte Loh
Jamie Smith
Jasper Snoek
M. Soljavcić
BDLAI4CE
387
17
0
23 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
75
1
0
28 Mar 2021
LiBRe: A Practical Bayesian Approach to Adversarial Detection
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Zhijie Deng
Xiao Yang
Shizhen Xu
Hang Su
Jun Zhu
BDLAAML
81
62
0
27 Mar 2021
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related
  Time Series
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Xing Han
S. Dasgupta
Joydeep Ghosh
AI4TS
79
34
0
25 Feb 2021
Bayesian Neural Network Priors Revisited
Bayesian Neural Network Priors Revisited
Vincent Fortuin
Adrià Garriga-Alonso
Sebastian W. Ober
F. Wenzel
Gunnar Rätsch
Richard Turner
Mark van der Wilk
Laurence Aitchison
BDLUQCV
133
141
0
12 Feb 2021
Continuous-Time Model-Based Reinforcement Learning
Continuous-Time Model-Based Reinforcement Learning
Çağatay Yıldız
Markus Heinonen
Harri Lähdesmäki
OffRL
71
58
0
09 Feb 2021
The Gaussian Neural Process
The Gaussian Neural Process
W. Bruinsma
James Requeima
Andrew Y. K. Foong
Jonathan Gordon
Richard Turner
BDL
77
29
0
10 Jan 2021
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness
  of Bayesian Neural Networks
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks
Arno Blaas
Stephen J. Roberts
BDLAAML
85
2
0
07 Jan 2021
An Active Learning Method for Diabetic Retinopathy Classification with
  Uncertainty Quantification
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification
Muhammad Ahtazaz Ahsan
A. Qayyum
Junaid Qadir
Adeel Razi
BDL
91
20
0
24 Dec 2020
Encoding the latent posterior of Bayesian Neural Networks for
  uncertainty quantification
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Gianni Franchi
Andrei Bursuc
Emanuel Aldea
Séverine Dubuisson
Isabelle Bloch
BDLUQCV
94
27
0
04 Dec 2020
All You Need is a Good Functional Prior for Bayesian Deep Learning
All You Need is a Good Functional Prior for Bayesian Deep Learning
Ba-Hien Tran
Simone Rossi
Dimitrios Milios
Maurizio Filippone
OODBDL
77
61
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 Challenges
Moloud Abdar
Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
Li Liu
...
Xiaochun Cao
Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
BDLUQCV
369
1,947
0
12 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
64
4
0
02 Nov 2020
Bayesian Deep Learning via Subnetwork Inference
Bayesian Deep Learning via Subnetwork Inference
Erik A. Daxberger
Eric T. Nalisnick
J. Allingham
Javier Antorán
José Miguel Hernández-Lobato
UQCVBDL
130
86
0
28 Oct 2020
Scalable Bayesian neural networks by layer-wise input augmentation
Scalable Bayesian neural networks by layer-wise input augmentation
Trung Trinh
Samuel Kaski
Markus Heinonen
UQCVBDL
36
3
0
26 Oct 2020
Out-of-distribution detection for regression tasks: parameter versus
  predictor entropy
Out-of-distribution detection for regression tasks: parameter versus predictor entropy
Y. Pequignot
Mathieu Alain
Patrick Dallaire
Alireza Yeganehparast
Pascal Germain
Josée Desharnais
Franccois Laviolette
OODD
36
2
0
24 Oct 2020
Incorporating Interpretable Output Constraints in Bayesian Neural
  Networks
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Wanqian Yang
Lars Lorch
Moritz Graule
Himabindu Lakkaraju
Finale Doshi-Velez
UQCVBDL
68
17
0
21 Oct 2020
Stationary Activations for Uncertainty Calibration in Deep Learning
Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi Meronen
Christabella Irwanto
Arno Solin
UQCVBDL
54
19
0
19 Oct 2020
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their
  Asymptotic Overconfidence
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
Agustinus Kristiadi
Matthias Hein
Philipp Hennig
BDL
63
9
0
06 Oct 2020
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