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Adversarial Attacks and Defences Competition
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To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.
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On the Robustness of Distributed Machine Learning against Transfer Attacks
Distributed ML with partitioned data and independently tuned learners improves robustness against transfer-based attacks over an ensemble baseline, but the abstract's emphasis on full heterogeneity is contradicted by ...
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