Sufficient Conditions for Idealised Models to Have No Adversarial
Examples: a Theoretical and Empirical Study with Bayesian Neural Networks

Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks

2 June 2018

Papers citing "Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks"

11 / 11 papers shown

Title
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Qizhang Li Yiwen Guo W. Zuo Hao Chen AAML 31 35 0 10 Feb 2023
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler Bertrand Charpentier Simon Geisler Daniel Zügner Stephan Günnemann UQCV BDL 41 80 0 26 Oct 2021
Who's Afraid of Thomas Bayes? Erick Galinkin AAML 28 0 0 30 Jul 2021
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties Lisa Schut Oscar Key R. McGrath Luca Costabello Bogdan Sacaleanu Medb Corcoran Y. Gal CML 26 47 0 16 Mar 2021
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 38 77 0 11 Feb 2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification Meet P. Vadera Satya Narayan Shukla B. Jalaeian Benjamin M. Marlin AAML BDL 17 6 0 07 Feb 2020
Universal adversarial examples in speech command classification Jon Vadillo Roberto Santana AAML 29 29 0 22 Nov 2019
Training Data Subset Search with Ensemble Active Learning Kashyap Chitta J. Álvarez Elmar Haussmann C. Farabet 22 13 0 29 May 2019
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting Artur Bekasov Iain Murray AAML BDL 20 14 0 29 Nov 2018
Sequential Neural Methods for Likelihood-free Inference Conor Durkan George Papamakarios Iain Murray BDL 36 24 0 21 Nov 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018