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Tackling covariate shift with node-based Bayesian neural networks

Tackling covariate shift with node-based Bayesian neural networks

6 June 2022
Trung Trinh
Markus Heinonen
Luigi Acerbi
Samuel Kaski
    BDL
    UQCV
ArXivPDFHTML

Papers citing "Tackling covariate shift with node-based Bayesian neural networks"

5 / 5 papers shown
Title
Improving robustness to corruptions with multiplicative weight
  perturbations
Improving robustness to corruptions with multiplicative weight perturbations
Trung Trinh
Markus Heinonen
Luigi Acerbi
Samuel Kaski
44
0
0
24 Jun 2024
On the optimization and pruning for Bayesian deep learning
On the optimization and pruning for Bayesian deep learning
X. Ke
Yanan Fan
BDL
UQCV
35
1
0
24 Oct 2022
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve
  Adversarial Robustness
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi
M. Shafiee
Michelle Karg
C. Scharfenberger
A. Wong
OOD
AAML
67
63
0
02 Mar 2020
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
276
5,675
0
05 Dec 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
287
9,145
0
06 Jun 2015
1