The Limitations of Model Uncertainty in Adversarial Settings

The Limitations of Model Uncertainty in Adversarial Settings

6 December 2018

Michael Backes

Papers citing "The Limitations of Model Uncertainty in Adversarial Settings"

9 / 9 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 32 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
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 91 0 30 Apr 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks L. Cardelli M. 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 9,138 0 06 Jun 2015