Robustness of Bayesian neural networks to gradient-based attacks
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, the problem remains open. In this paper, we analyse the geometry of adversarial attacks in the large-dat...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation, Inc.
2020
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