Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification

7 February 2020

Papers citing "Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification"

6 / 6 papers shown

Title
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities Andrea Tocchetti Lorenzo Corti Agathe Balayn Mireia Yurrita Philip Lippmann Marco Brambilla Jie-jin Yang 27 10 0 17 Oct 2022
Tackling covariate shift with node-based Bayesian neural networks Trung Trinh Markus Heinonen Luigi Acerbi Samuel Kaski BDL UQCV 8 6 0 06 Jun 2022
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems Meet P. Vadera Benjamin M. Marlin UQCV BDL 18 5 0 03 Dec 2021
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks Meet P. Vadera Adam D. Cobb B. Jalaeian Benjamin M. Marlin BDL UQCV 27 16 0 08 Jul 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,842 0 08 Jul 2016