SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[AM18] Naveed Akhtar and Ajmal Mian. “Threat of adversarial attacks on deep learning in computer vision: A survey”. In: *IEEE Access 6* (2018), pp. 14410–14430 (Cited on pages 2, 9).
[AL21] Zeyuan Allen-Zhu and Yuanzhi Li. “Feature purification: How adversarial training performs robust deep learning”. In: IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS). 2021 (Cited on page 3).
[ACW18] Anish Athalye, Nicholas Carlini, and David Wagner. “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples”. In: International conference on machine learning (ICML). 2018 (Cited on page 1).
[BBC21] Peter Bartlett, Sébastien Bubeck, and Yeshwanth Cherapanamjeri. “Adversarial examples in multi-layer random relu networks”. In: Advances in Neural Information Processing Systems (NeurIPS) (2021) (Cited on page 3).
[Bub+21] Sébastien Bubeck, Yeshwanth Cherapanamjeri, Gauthier Gidel, and Rémi Tachet des Combes. “A single gradient step finds adversarial examples on random two-layers neural networks”. In: Advances in Neural Information Processing Systems (NeurIPS) (2021) (Cited on page 3).
[Bub+19] Sébastien Bubeck, Yin Tat Lee, Eric Price, and Ilya Razenshteyn. "Adversarial examples from computational constraints". In: International Conference on Machine Learning (ICML). 2019 (Cited on page 3).
[BLN21] Sébastien Bubeck, Yuanzhi Li, and Dheeraj M Nagaraj. “A law of robustness for two-layers neural networks”. In: Conference on Learning Theory (COLT). 2021 (Cited on pages 3, 8).