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A Primer on Bayesian Neural Networks: Review and Debates

A Primer on Bayesian Neural Networks: Review and Debates

28 September 2023
Federico Danieli
Konstantinos Pitas
M. Vladimirova
Vincent Fortuin
    BDL
    AAML
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Papers citing "A Primer on Bayesian Neural Networks: Review and Debates"

14 / 14 papers shown
Title
Posterior and variational inference for deep neural networks with heavy-tailed weights
Posterior and variational inference for deep neural networks with heavy-tailed weights
Ismael Castillo
Paul Egels
BDL
48
3
0
05 Jun 2024
Bayesian RG Flow in Neural Network Field Theories
Bayesian RG Flow in Neural Network Field Theories
Jessica N. Howard
Marc S. Klinger
Anindita Maiti
A. G. Stapleton
50
1
0
27 May 2024
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks
Youngkyoung Bae
Seungwoong Ha
Hawoong Jeong
71
2
0
02 Feb 2024
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
BDL
AAML
72
8
0
27 May 2022
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Pierre Wolinski
Julyan Arbel
AI4CE
65
8
0
24 May 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi
Pavel Izmailov
Gregory W. Benton
Micah Goldblum
A. Wilson
UQCV
BDL
42
55
0
23 Feb 2022
Bayesian neural network unit priors and generalized Weibull-tail
  property
Bayesian neural network unit priors and generalized Weibull-tail property
M. Vladimirova
Julyan Arbel
Stéphane Girard
BDL
47
9
0
06 Oct 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
BDL
AAML
42
2
0
07 Jan 2021
Why bigger is not always better: on finite and infinite neural networks
Why bigger is not always better: on finite and infinite neural networks
Laurence Aitchison
170
51
0
17 Oct 2019
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan
Didrik Nielsen
Voot Tangkaratt
Wu Lin
Y. Gal
Akash Srivastava
ODL
74
264
0
13 Jun 2018
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
228
2,231
0
24 Jun 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,635
0
05 Dec 2016
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
123
600
0
14 Feb 2016
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
247
9,042
0
06 Jun 2015
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