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Flexible Bayesian Nonlinear Model Configuration
v1v2 (latest)

Flexible Bayesian Nonlinear Model Configuration

Journal of Artificial Intelligence Research (JAIR), 2020
5 March 2020
A. Hubin
G. Storvik
F. Frommlet
    BDL
ArXiv (abs)PDFHTML

Papers citing "Flexible Bayesian Nonlinear Model Configuration"

19 / 19 papers shown
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
Eirik Høyheim
Lars Skaaret-Lund
Solve Sæbø
A. Hubin
UQCVBDL
298
1
0
13 Mar 2025
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou
Maria Skoularidou
Konstantina Palla
Laurence Aitchison
Julyan Arbel
...
David Rügamer
Yee Whye Teh
Max Welling
Andrew Gordon Wilson
Ruqi Zhang
UQCVBDL
507
66
0
01 Feb 2024
Subsampling MCMC for Bayesian Variable Selection and Model Averaging in
  BGNLM
Subsampling MCMC for Bayesian Variable Selection and Model Averaging in BGNLM
Jon Lachmann
A. Hubin
BDL
113
1
0
28 Dec 2023
Fractional Polynomials Models as Special Cases of Bayesian Generalized
  Nonlinear Models
Fractional Polynomials Models as Special Cases of Bayesian Generalized Nonlinear Models
A. Hubin
Georg Heinze
R. D. Bin
149
0
0
25 May 2023
Variational Inference for Bayesian Neural Networks under Model and
  Parameter Uncertainty
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
A. Hubin
G. Storvik
BDLUQCV
428
6
0
01 May 2023
Reversible Genetically Modified Mode Jumping MCMC
Reversible Genetically Modified Mode Jumping MCMC
A. Hubin
F. Frommlet
G. Storvik
150
0
0
11 Oct 2021
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational BayesNeural Information Processing Systems (NeurIPS), 2020
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
1.8K
20,656
0
17 Feb 2020
Explaining individual predictions when features are dependent: More
  accurate approximations to Shapley values
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
K. Aas
Martin Jullum
Anders Løland
FAttTDI
443
877
0
25 Mar 2019
A novel algorithmic approach to Bayesian Logic Regression
A novel algorithmic approach to Bayesian Logic Regression
A. Hubin
G. Storvik
F. Frommlet
319
18
0
22 May 2017
Estimating the marginal likelihood with Integrated nested Laplace
  approximation (INLA)
Estimating the marginal likelihood with Integrated nested Laplace approximation (INLA)
A. Hubin
G. Storvik
235
22
0
04 Nov 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional NetworksComputer Vision and Pattern Recognition (CVPR), 2016
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
2.2K
42,651
0
25 Aug 2016
Mode jumping MCMC for Bayesian variable selection in GLMM
Mode jumping MCMC for Bayesian variable selection in GLMM
A. Hubin
G. Storvik
288
32
0
21 Apr 2016
Speeding Up MCMC by Delayed Acceptance and Data Subsampling
Speeding Up MCMC by Delayed Acceptance and Data Subsampling
M. Quiroz
Minh-Ngoc Tran
M. Villani
Robert Kohn
409
43
0
22 Jul 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCVBDL
929
2,099
0
20 May 2015
Highway Networks
Highway Networks
R. Srivastava
Klaus Greff
Jürgen Schmidhuber
1.1K
1,880
0
03 May 2015
Ergodicity of Approximate MCMC Chains with Applications to Large Data
  Sets
Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets
Natesh S. Pillai
Aaron Smith
353
59
0
01 May 2014
Speeding Up MCMC by Efficient Data Subsampling
Speeding Up MCMC by Efficient Data SubsamplingJournal of the American Statistical Association (JASA), 2014
M. Quiroz
Robert Kohn
M. Villani
Minh-Ngoc Tran
484
181
0
16 Apr 2014
Bayes and empirical-Bayes multiplicity adjustment in the
  variable-selection problem
Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
James G. Scott
J. Berger
357
608
0
10 Nov 2010
A comparison of the Benjamini-Hochberg procedure with some Bayesian
  rules for multiple testing
A comparison of the Benjamini-Hochberg procedure with some Bayesian rules for multiple testing
M. Bogdan
J. Ghosh
S. Tokdar
438
106
0
16 May 2008
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