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Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle.
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M. Alzantot, Y. Sharma, A. Elgohary, B.-J. Ho, M. Srivastava, and K.-W. Chang, “Generating natural language adversarial examples,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 2890–2896. [Online]. Available: https://www.aclweb.org/anthology/D18-1316
2018
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Y. Vorobeychik, M. Kantarcioglu, R. Brachman, P. Stone, and F. Rossi, Adversarial Machine Learning , ser. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2018. [Online]. Available: https://books.google.ca/books?id=Lw5pDwAAQBAJ
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A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=rJzIBfZAb
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2019
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J.-B. Alayrac, J. Uesato, P.-S. Huang, A. Fawzi, R. Stanforth, and P. Kohli, “Are labels required for improving adversarial robustness?” in Advances in Neural Information Processing Systems , 2019, pp. 12 214–12 223
2019
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F. Croce and M. Hein, “Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,” in International Conference on Machine Learning . PMLR, 2020, pp. 2206–2216
2020
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2020
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2020
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D. Wu, S.-T. Xia, and Y. Wang, “Adversarial weight perturbation helps robust generalization,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
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2020
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M. Atzmon, N. Haim, L. Yariv, O. Israelov, H. Maron, and Y. Lipman, “Controlling neural level sets,” in Advances in Neural Information Processing Systems , 2019, pp. 2034–2043
2043
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F. Croce, M. Andriushchenko, and M. Hein, “Provable robustness of relu networks via maximization of linear regions,” in the 22nd International Conference on Artificial Intelligence and Statistics . PMLR, 2019, pp. 2057–2066
2066
Closest in time.