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Modern deep neural network models suffer from adversarial examples, i.e.
Bayesian Learning for Neural Networks
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Rawat, A., Wistuba, M., and Nicolae, M.-I. (2017) · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D. (2018) · 2018
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Sufficient conditions for idealised models to have no adversarial examples: a theoretical and empirical study with Bayesian neural networks
Gal, Y. and Smith, L. (2018) · 2018
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Gilmer, J., Metz, L., Faghri, F., Schoenholz, S. S., Raghu, M., Wattenberg, M., and Goodfellow, I. (2018) · 2018
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Bradshaw, J., de G. Matthews, A. G., and Ghahramani, Z. (2017) · 2017
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