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All You Need is a Good Functional Prior for Bayesian Deep Learning
v1v2 (latest)

All You Need is a Good Functional Prior for Bayesian Deep Learning

Journal of machine learning research (JMLR), 2020
25 November 2020
Ba-Hien Tran
Simone Rossi
Dimitrios Milios
Maurizio Filippone
    OODBDL
ArXiv (abs)PDFHTML

Papers citing "All You Need is a Good Functional Prior for Bayesian Deep Learning"

38 / 38 papers shown
Cross-Modal Alignment via Variational Copula Modelling
Cross-Modal Alignment via Variational Copula Modelling
Feng Wu
Tsai Hor Chan
Fuying Wang
Guosheng Yin
Lequan Yu
175
0
0
05 Nov 2025
Variational Polya Tree
Variational Polya Tree
Lu Xu
Tsai Hor Chan
Kwok Fai Lam
Lequan Yu
Guosheng Yin
BDL
188
0
0
26 Oct 2025
The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters
The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters
Scott Koermer
Natalie Klein
200
0
0
24 Sep 2025
Simulation Priors for Data-Efficient Deep Learning
Simulation Priors for Data-Efficient Deep Learning
Lenart Treven
Bhavya Sukhija
Jonas Rothfuss
Stelian Coros
Florian Dorfler
Andreas Krause
210
0
0
06 Sep 2025
Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
Cody Grogan
Som Dhulipala
Mauricio Tano
Izabela Gutowska
Som Dutta
UQCV
164
0
0
23 Aug 2025
Hi-fi functional priors by learning activations
Hi-fi functional priors by learning activations
Marcin Sendera
Amin Sorkhei
Tomasz Kuśmierczyk
132
0
0
12 Aug 2025
laplax -- Laplace Approximations with JAX
laplax -- Laplace Approximations with JAX
Tobias Weber
Bálint Mucsányi
Lenard Rommel
Thomas Christie
Lars Kasüschke
Marvin Pfortner
Philipp Hennig
BDL
227
3
0
22 Jul 2025
Scaling Laws for Uncertainty in Deep Learning
Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso
Simone Rossi
Giulio Franzese
Markus Heinonen
Maurizio Filippone
BDLUQCV
271
0
0
11 Jun 2025
Optimizing Data Augmentation through Bayesian Model Selection
Optimizing Data Augmentation through Bayesian Model Selection
Madi Matymov
Ba-Hien Tran
Michael Kampffmeyer
Markus Heinonen
Maurizio Filippone
344
1
0
27 May 2025
Streamlining Prediction in Bayesian Deep Learning
Streamlining Prediction in Bayesian Deep LearningInternational Conference on Learning Representations (ICLR), 2024
Marcus Klasson
Talal Alrawajfeh
Mikko Heikkilä
Martin Trapp
UQCVBDL
718
5
0
27 Nov 2024
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Steven Adams
A. Patané
Morteza Lahijanian
Luca Laurenti
427
8
0
26 Jul 2024
Bayesian Entropy Neural Networks for Physics-Aware Prediction
Bayesian Entropy Neural Networks for Physics-Aware Prediction
R. Rathnakumar
Jiayu Huang
Hao Yan
Yongming Liu
343
3
0
01 Jul 2024
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
Kushagra Pandey
UQCVBDL
558
4
0
06 Jun 2024
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Xiumei Deng
Jun Li
Kang Wei
Long Shi
Zeihui Xiong
Ming Ding
Wen Chen
Shi Jin
H. Vincent Poor
FedML
288
1
0
28 May 2024
Making Better Use of Unlabelled Data in Bayesian Active Learning
Making Better Use of Unlabelled Data in Bayesian Active Learning
Freddie Bickford-Smith
Adam Foster
Tom Rainforth
378
10
0
26 Apr 2024
On Uncertainty Quantification for Near-Bayes Optimal Algorithms
On Uncertainty Quantification for Near-Bayes Optimal Algorithms
Ziyu Wang
Chris Holmes
UQCV
336
3
0
28 Mar 2024
Spatial Bayesian Neural Networks
Spatial Bayesian Neural Networks
A. Zammit‐Mangion
Michael D. Kaminski
Ba-Hien Tran
Maurizio Filippone
Noel Cressie
BDL
304
14
0
16 Nov 2023
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent
  Representations
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent RepresentationsNeural Information Processing Systems (NeurIPS), 2023
Tsai Hor Chan
Kin Wai Lau
Jiajun Shen
Guosheng Yin
Lequan Yu
UQCVOOD
261
2
0
25 Oct 2023
On the meaning of uncertainty for ethical AI: philosophy and practice
On the meaning of uncertainty for ethical AI: philosophy and practice
Cassandra Bird
Daniel Williamson
Sabina Leonelli
210
1
0
11 Sep 2023
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
Disha Makhija
Joydeep Ghosh
Nhat Ho
FedML
255
3
0
13 Jun 2023
Solution of physics-based inverse problems using conditional generative
  adversarial networks with full gradient penalty
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penaltyComputer Methods in Applied Mechanics and Engineering (CMAME), 2023
Deep Ray
Javier Murgoitio-Esandi
Agnimitra Dasgupta
Assad A. Oberai
GAN
251
18
0
08 Jun 2023
Incorporating Unlabelled Data into Bayesian Neural Networks
Incorporating Unlabelled Data into Bayesian Neural Networks
Mrinank Sharma
Tom Rainforth
Yee Whye Teh
Vincent Fortuin
SSLUQCVBDL
342
10
0
04 Apr 2023
Scalable Stochastic Gradient Riemannian Langevin Dynamics in
  Non-Diagonal Metrics
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Hanlin Yu
M. Hartmann
Bernardo Williams
Arto Klami
BDL
464
9
0
09 Mar 2023
Natural Gradient Hybrid Variational Inference with Application to Deep
  Mixed Models
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed ModelsStatistics and computing (Stat. Comput.), 2023
Weiben Zhang
M. Smith
Worapree Maneesoonthorn
Rubén Loaiza-Maya
167
2
0
27 Feb 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Fully Bayesian Autoencoders with Latent Sparse Gaussian ProcessesInternational Conference on Machine Learning (ICML), 2023
Ba-Hien Tran
Babak Shahbaba
Stephan Mandt
Maurizio Filippone
SyDaBDLUQCV
265
8
0
09 Feb 2023
Diffusion Generative Models in Infinite Dimensions
Diffusion Generative Models in Infinite DimensionsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Gavin Kerrigan
Justin Ley
Padhraic Smyth
DiffM
423
49
0
01 Dec 2022
On the detrimental effect of invariances in the likelihood for
  variational inference
On the detrimental effect of invariances in the likelihood for variational inferenceNeural Information Processing Systems (NeurIPS), 2022
Richard Kurle
R. Herbrich
Tim Januschowski
Bernie Wang
Jan Gasthaus
336
9
0
15 Sep 2022
Approximate blocked Gibbs sampling for Bayesian neural networks
Approximate blocked Gibbs sampling for Bayesian neural networksStatistics and computing (Stat. Comput.), 2022
Theodore Papamarkou
BDL
965
3
0
24 Aug 2022
Variational Inference of overparameterized Bayesian Neural Networks: a
  theoretical and empirical study
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study
Tom Huix
Szymon Majewski
Alain Durmus
Eric Moulines
Anna Korba
BDL
245
7
0
08 Jul 2022
Incorporating functional summary information in Bayesian neural networks
  using a Dirichlet process likelihood approach
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approachInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Vishnu Raj
Tianyu Cui
Markus Heinonen
Pekka Marttinen
UQCVBDL
285
1
0
04 Jul 2022
Feature Space Particle Inference for Neural Network Ensembles
Feature Space Particle Inference for Neural Network EnsemblesInternational Conference on Machine Learning (ICML), 2022
Shingo Yashima
Teppei Suzuki
Kohta Ishikawa
Ikuro Sato
Rei Kawakami
BDL
355
12
0
02 Jun 2022
On out-of-distribution detection with Bayesian neural networks
On out-of-distribution detection with Bayesian neural networks
Francesco DÁngelo
Christian Henning
BDLUQCV
356
10
0
12 Oct 2021
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
Revisiting the Effects of Stochasticity for Hamiltonian SamplersInternational Conference on Machine Learning (ICML), 2021
Giulio Franzese
Dimitrios Milios
Maurizio Filippone
Pietro Michiardi
256
3
0
30 Jun 2021
Model Selection for Bayesian Autoencoders
Model Selection for Bayesian AutoencodersNeural Information Processing Systems (NeurIPS), 2021
Ba-Hien Tran
Simone Rossi
Dimitrios Milios
Pietro Michiardi
Edwin V. Bonilla
Maurizio Filippone
BDL
252
14
0
11 Jun 2021
Learning Functional Priors and Posteriors from Data and Physics
Learning Functional Priors and Posteriors from Data and Physics
Xuhui Meng
Liu Yang
Zhiping Mao
J. Ferrandis
George Karniadakis
AI4CE
466
66
0
08 Jun 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A ReviewInternational Statistical Review (ISR), 2021
Vincent Fortuin
UQCVBDL
549
167
0
14 May 2021
What Are Bayesian Neural Network Posteriors Really Like?
What Are Bayesian Neural Network Posteriors Really Like?International Conference on Machine Learning (ICML), 2021
Pavel Izmailov
Sharad Vikram
Matthew D. Hoffman
A. Wilson
UQCVBDL
459
449
0
29 Apr 2021
Bayesian Neural Network Priors Revisited
Bayesian Neural Network Priors RevisitedInternational Conference on Learning Representations (ICLR), 2021
Vincent Fortuin
Adrià Garriga-Alonso
Sebastian W. Ober
F. Wenzel
Gunnar Rätsch
Richard Turner
Mark van der Wilk
Laurence Aitchison
BDLUQCV
448
159
0
12 Feb 2021
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