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1812.02606
Cited By
The Limitations of Model Uncertainty in Adversarial Settings
6 December 2018
Kathrin Grosse
David Pfaff
M. Smith
Michael Backes
AAML
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Papers citing
"The Limitations of Model Uncertainty in Adversarial Settings"
10 / 10 papers shown
Title
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Yunzhen Feng
Tim G. J. Rudner
Nikolaos Tsilivis
Julia Kempe
AAML
BDL
43
1
0
27 Apr 2024
Adversarial Attacks Against Uncertainty Quantification
Emanuele Ledda
Daniele Angioni
Giorgio Piras
Giorgio Fumera
Battista Biggio
Fabio Roli
AAML
35
2
0
19 Sep 2023
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
Ruoxi Qin
Linyuan Wang
Xuehui Du
Xing-yuan Chen
Binghai Yan
AAML
26
0
0
01 Aug 2023
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Zhijie Deng
Xiao Yang
Shizhen Xu
Hang Su
Jun Zhu
BDL
AAML
20
61
0
27 Mar 2021
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka
Joshua Patterson
Corey J. Nolet
AI4CE
24
483
0
12 Feb 2020
Test Selection for Deep Learning Systems
Wei Ma
Mike Papadakis
Anestis Tsakmalis
Maxime Cordy
Yves Le Traon
OOD
21
92
0
30 Apr 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks
L. Cardelli
Marta Kwiatkowska
Luca Laurenti
Nicola Paoletti
A. Patané
Matthew Wicker
AAML
31
54
0
05 Mar 2019
Adversarial Attack and Defense on Point Sets
Jiancheng Yang
Qiang Zhang
Rongyao Fang
Bingbing Ni
Jinxian Liu
Qi Tian
3DPC
24
122
0
28 Feb 2019
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
68
171
0
08 Jul 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
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