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Nicholas Carlini
Researcher at Google
Publications - 104
Citations - 24459
Nicholas Carlini is an academic researcher from Google. The author has contributed to research in topics: Computer science & Robustness (computer science). The author has an hindex of 40, co-authored 78 publications receiving 15330 citations. Previous affiliations of Nicholas Carlini include University of California, Berkeley.
Papers
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Proceedings ArticleDOI
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini,David Wagner +1 more
TL;DR: In this paper, the authors demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability.
Posted Content
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
TL;DR: This work identifies obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples, and develops attack techniques to overcome this effect.
Proceedings ArticleDOI
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini,David Wagner +1 more
TL;DR: In this paper, the authors survey ten recent proposals for adversarial examples and compare their efficacy, concluding that all can be defeated by constructing new loss functions, and propose several simple guidelines for evaluating future proposed defenses.
Posted Content
MixMatch: A Holistic Approach to Semi-Supervised Learning
TL;DR: MixMatch as discussed by the authors predicts low-entropy labels for unlabeled examples and combines them with labeled and unlabelled data using MixUp to obtain state-of-the-art results.
Proceedings Article
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn,David Berthelot,Chun-Liang Li,Zizhao Zhang,Nicholas Carlini,Ekin D. Cubuk,Alex Kurakin,Han Zhang,Colin Raffel +8 more
TL;DR: This paper demonstrates the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling, and shows that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks.