Feb 10, 2021 · In an empirical investigation, we demonstrate that the presented approach enables training of certifiably robust models on MNIST, FashionMNIST ...
We consider adversarial training of deep neu- ral networks through the lens of Bayesian learn- ing, and present a principled framework for ad-.
In an empirical investigation, we demonstrate that the presented approach enables training of certifiably robust models on MNIST, FashionMNIST, and CIFAR-10 and ...
Jun 23, 2023 · Abstract:We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations.
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Feb 23, 2021 · We consider adversarial training of deep neu- ral networks through the lens of Bayesian learn- ing, and present a principled framework for ...
The approach is based on weight interval sampling, integration, and bound propagation techniques, and can be applied to BNNs with a large number of ...
Code for paper "Bayesian Inference with Certified Adversarial Robustness" ... This code contains the custom inference and analysis methods for inferring ...
A novel robust training framework is proposed to alleviate the issue of vulnerability to adversarial attacks, Bayesian Robust Learning, ...
Here, we study the adversarial robustness of amortized Bayesian inference, focusing on simulation-based estimation of multi-dimensional posterior distributions.
Missing: Certifiable | Show results with:Certifiable
Jun 23, 2023 · We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations.