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Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?, , , , , , and . ICLR, OpenReview.net, (2022)Parameterizing Activation Functions for Adversarial Robustness., , and . CoRR, (2021)Formulating Robustness Against Unforeseen Attacks., , and . NeurIPS, (2022)Larimar: Large Language Models with Episodic Memory Control., , , , , , , , , and 2 other author(s). CoRR, (2024)Parameterizing Activation Functions for Adversarial Robustness., , and . SP (Workshops), page 80-87. IEEE, (2022)MultiRobustBench: Benchmarking Robustness Against Multiple Attacks., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 6760-6785. PMLR, (2023)Improving Adversarial Robustness Using Proxy Distributions., , , , , , and . CoRR, (2021)PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses., , , , , and . CoRR, (2023)Neural Networks with Recurrent Generative Feedback., , , , , , and . NeurIPS, (2020)Out-of-Distribution Detection Using Neural Rendering Generative Models., , , , and . CoRR, (2019)