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Adversarial Robustness is at Odds with Lazy Training

18 June 2022

Papers citing "Adversarial Robustness is at Odds with Lazy Training"

24 / 24 papers shown

Title
Gradient Methods Provably Converge to Non-Robust Networks Gal Vardi Gilad Yehudai Ohad Shamir 83 28 0 09 Feb 2022
Adversarial Examples in Multi-Layer Random ReLU Networks Peter L. Bartlett Sébastien Bubeck Yeshwanth Cherapanamjeri AAML GAN 55 29 0 23 Jun 2021
A single gradient step finds adversarial examples on random two-layers neural networks Sébastien Bubeck Yeshwanth Cherapanamjeri Gauthier Gidel Rémi Tachet des Combes MLT 58 28 0 08 Apr 2021
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 285 838 0 19 Feb 2020
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? Zixiang Chen Yuan Cao Difan Zou Quanquan Gu 75 123 0 27 Nov 2019
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks Ziwei Ji Matus Telgarsky 72 178 0 26 Sep 2019
Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao Tianle Cai Haochuan Li Liwei Wang Cho-Jui Hsieh Jason D. Lee AAML 101 109 0 19 Jun 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 98 905 0 18 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 86 158 0 04 Feb 2019
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance A. Shamir Itay Safran Eyal Ronen O. Dunkelman GAN AAML 40 95 0 30 Jan 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 208 974 0 24 Jan 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 839 0 19 Dec 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 266 1,469 0 09 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 233 1,276 0 04 Oct 2018
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding Lea Schonherr Katharina Kohls Steffen Zeiler Thorsten Holz D. Kolossa AAML 79 291 0 16 Aug 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 273 3,219 0 20 Jun 2018
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos Chawin Sitawarin A. Bhagoji Arsalan Mosenia Prateek Mittal M. Chiang AAML 52 69 0 09 Jan 2018
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 163 2,160 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 317 12,131 0 19 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,864 0 20 May 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,583 0 16 Aug 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 115 3,967 0 24 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,121 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 282 14,963 1 21 Dec 2013