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Attacking Bayes: On the Adversarial Robustness of Bayesian Neural
  Networks

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks

27 April 2024
Yunzhen Feng
Tim G. J. Rudner
Nikolaos Tsilivis
Julia Kempe
    AAML
    BDL
ArXivPDFHTML

Papers citing "Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks"

11 / 11 papers shown
Title
Tractable Function-Space Variational Inference in Bayesian Neural
  Networks
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Tim G. J. Rudner
Zonghao Chen
Yee Whye Teh
Y. Gal
62
39
0
28 Dec 2023
Function-Space Regularization in Neural Networks: A Probabilistic
  Perspective
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
Tim G. J. Rudner
Sanyam Kapoor
Shikai Qiu
A. Wilson
24
12
0
28 Dec 2023
Informative Priors Improve the Reliability of Multimodal Clinical Data
  Classification
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
L. J. L. Lopez
Tim G. J. Rudner
Karan Singhal
30
3
0
17 Nov 2023
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
Huimin Zeng
Zhenrui Yue
Yang Zhang
Ziyi Kou
Lanyu Shang
Dong Wang
OOD
AAML
25
7
0
03 Oct 2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative
  Priors
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Ravid Shwartz-Ziv
Micah Goldblum
Hossein Souri
Sanyam Kapoor
Chen Zhu
Yann LeCun
A. Wilson
UQCV
BDL
54
43
0
20 May 2022
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
44
2
0
07 Jan 2021
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
207
668
0
19 Oct 2020
Generating Natural Language Adversarial Examples
Generating Natural Language Adversarial Examples
M. Alzantot
Yash Sharma
Ahmed Elgohary
Bo-Jhang Ho
Mani B. Srivastava
Kai-Wei Chang
AAML
233
909
0
21 Apr 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,813
0
08 Jul 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
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
130
3,260
0
09 Jun 2012
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