SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[1] J. Bruna, C. Szegedy, I. Sutskever, I. Goodfellow, W. Zaremba, R. Fergus, and D. Erhan, "Intriguing properties of neural networks," in International Conference on Learning Representations (ICLR), 2014.
[2] I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” in International Conference on Learning Representations (ICLR), 2015.
[3] A. Kurakin, I. Goodfellow, S. Bengio, et al., “Adversarial attacks and defences competition,” in The NIPS '17 competition: Building Intelligent Systems, 2018, pp. 195-231.
[4] A. Fawzi, O. Fawzi, and P. Frossard, "Analysis of classifiers' robustness to adversarial perturbations," Machine Learning, vol. 107, no. 3, pp. 481-508, 2018.
[5] A. Ilyas, S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry, "Adversarial Examples Are Not Bugs, They Are Features," Advances in Neural Information Processing Systems, vol. 32, 2019.
[6] X. Yuan, P. He, Q. Zhu, and X. Li, "Adversarial examples: Attacks and defenses for deep learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2805-2824, 2019.
[7] A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, "Towards Deep Learning Models Resistant to Adversarial Attacks," International Conference for Learning Representations (ICLR), 2018.
[8] R. Huang, B. Xu, D. Schuurmans, and C. Szepesvari, “Learning with a Strong Adversary,” arXiv:1511.03034, 2016.
[9] T. Bai, J. Luo, J. Zhao, B. Wen, and Q. Wang, “Recent Advances in Adversarial Training for Adversarial Robustness,” arXiv:2102.01356, 2021.
[10] F. Croce, M. Andriushchenko, V. Sehwag, E. Debenedetti, N. Flammarion, M. Chiang, P. Mittal, and M. Hein, "RobustBench: A standardized adversarial robustness benchmark," in NeurIPS Datasets and Benchmarks track, 2021.
[11] H. Taheri, R. Pedarsani, and C. Thrampoulidis, "Asymptotic Behavior of Adversarial Training in Binary Classification," IEEE International Symposium on Information Theory (ISIT), vol. 127-132, 2022.
[12] A. Javanmard, M. Soltanolkotabi, and H. Hassani, “Precise tradeoffs in adversarial training for linear regression,” in Proceedings of the Conference on Learning Theory, vol. 125, 2020, pp. 2034–2078.
[13] H. Hassani and A. Javanmard, “The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression,” arXiv:2201.05149, 2022.
[14] Y. Min, L. Chen, and A. Karbasi, “The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization,” Proceedings of the Conference on Uncertainty in Artificial Intelligence, vol. 161, pp. 129–139, 2021.
[15] D. Yin, R. Kannan, and P. Bartlett, "Rademacher Complexity for Adversarily Robust Generalization," in Proceeding of the International Conference on Machine Learning, 2019, pp. 7085-7094.
[16] D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Ma, “Robustness May Be At Odds with Accuracy,” International Conference for Learning Representations, 2019.
[17] A. H. Ribeiro and T. B. Schön, “Overparameterized Linear Regression under Adversarial Attacks,” IEEE Transactions on Signal Processing, 2023.
[45] A. M. Saxe, J. L. McClelland, and S. Ganguli, "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks," International Conference on Learning Representations (ICLR), 2014.
[46] T. Laurent and J. Brecht, “Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global,” in Proceedings of the International Conference on Machine Learning, 2018, pp. 2902–2907.
[47] J. Lee, L. Xiao, S. S. Schoenholz, Y. Bahri, R. Novak, J. Sohl-Dickstein, and J. Pennington, "Wide neural networks of any depth evolve as linear models under gradient descent," Journal of Statistical Mechanics: Theory and Experiment, vol. 2020, no. 12, p. 124002, 2020.
[48] A. Jacot, F. Gabriel, and C. Hongler, "Neural Tangent Kernel: Convergence and Generalization in Neural Networks," Advances in Neural Information Processing Systems 31, 2018.
[49] D. Jakubovitz and R. Giryes, "Improving DNN robustness to adversarial attacks using jacobian regularization," in European conference on computer vision, 2018.