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• W3018577897 abstract "We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations. Given a compact set of input points, $T subseteq mathbb{R}^m$, we study the probability w.r.t. the BNN posterior that all the points in $T$ are mapped to the same region $S$ in the output space. In particular, this can be used to evaluate the probability that a network sampled from the BNN is vulnerable to adversarial attacks. We rely on relaxation techniques from non-convex optimization to develop a method for computing a lower bound on probabilistic safety for BNNs, deriving explicit procedures for the case of interval and linear function propagation techniques. We apply our methods to BNNs trained on a regression task, airborne collision avoidance, and MNIST, empirically showing that our approach allows one to certify probabilistic safety of BNNs with millions of parameters." @default.
• W3018577897 created "2020-05-01" @default.
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• W3018577897 date "2020-04-21" @default.
• W3018577897 modified "2023-10-18" @default.
• W3018577897 title "Probabilistic Safety for Bayesian Neural Networks" @default.
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• W3018577897 hasPublicationYear "2020" @default.
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