@inproceedings{e42c144f2c5c4a2ca40e55d273b1a4cc,
title = "Gradient Methods Provably Converge to Non-Robust Networks",
abstract = "Despite a great deal of research, it is still unclear why neural networks are so susceptible to adversarial examples. In this work, we identify natural settings where depth-2 ReLU networks trained with gradient flow are provably non-robust (susceptible to small adversarial `2-perturbations), even when robust networks that classify the training dataset correctly exist. Perhaps surprisingly, we show that the well-known implicit bias towards margin maximization induces bias towards non-robust networks, by proving that every network which satisfies the KKT conditions of the max-margin problem is non-robust.",
author = "Gal Vardi and Gilad Yehudai and Ohad Shamir",
note = "Publisher Copyright: {\textcopyright} 2022 Neural information processing systems foundation. All rights reserved.; 36th Conference on Neural Information Processing Systems, NeurIPS 2022 ; Conference date: 28-11-2022 Through 09-12-2022",
year = "2022",
language = "الإنجليزيّة",
series = "Advances in Neural Information Processing Systems",
editor = "S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh",
booktitle = "Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022",
}