[1] Sara Atito Ali Ahmed, Muhammad Awais, and Josef Kittler. Sit: Self-supervised vision transformer. Computing Research Repository, 2021.
[2] Roman Bachmann, David Mizrahi, Andrei Atanov, and Amir Zamir. Multimode multi-task masked autoencoders. arXiv preprint arXiv:2204.01678, 2022.
[3] Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. data2vec: A general framework for self-supervised learning in speech, vision and language. Computing Research Repository, 2022.
[4] Adil M Bagirov, Bülent Karasözen, and Meral Sezer. Discrete gradient method: derivative-free method for nonsmooth optimization. Journal of Optimization Theory and Applications, 2008.
[5] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers. In International Conference on Learning Representations (ICLR), 2021.
[6] Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Mellenberg, and Gijs Rennen. Robust solutions of optimization problems affected by uncertain probabilities. Management Science, 2013.
[7] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. "O'Reilly Media, Inc.", 2009.
[8] Ali Borji. A categorical archive of chatgpt failures. arXiv preprint arXiv:2302.03494, 2023.
[9] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems (NeurIPS), 2020.
[10] Sébastien Bubeck et al. Convex optimization: Algorithms and complexity. Foundations and Trends in Machine Learning, 2015.
[11] Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, and Alexey Kurakin. On evaluating adversarial robustness. Computing Research Repository, 2019.
[12] Jacques Antonin Chatelon, Donald W Hearn, and Timothy J Lowe. A subgradient algorithm for certain minimax and minisum problems. Mathematical Programming, 1978.
[13] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[14] Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In International Conference on Machine Learning (ICML), pages 1691-1703. PMLR, 2020.
[15] Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, and Zhangyang Wang. Adversarial robustness: From self-supervised pre-training to fine-tuning. In Conference on Computer Vision and Pattern Recognition (CVPR), pages 699-708, 2020.
[16] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International Conference on Machine Learning (ICML), 2018.
[17] Jeremy Cohen, *Elan Rosenfeld, and Zico Kolter*. Certified adversarial robustness via randomized smoothing. In *International Conference on Machine Learning (ICML)*, pages 1310–1320. PMLR, 2019.
[18] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2019.