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Adversarial Robustness Against the Union of Multiple Perturbation Models.

, , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 6640-6650. PMLR, (2020)

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Rethinking LLM Memorization through the Lens of Adversarial Compression., , , , и . CoRR, (2024)Understanding Hallucinations in Diffusion Models through Mode Interpolation., , , и . CoRR, (2024)Data-Free Model Extraction., , , и . CoRR, (2020)Why and when should you pool? Analyzing Pooling in Recurrent Architectures., , , и . EMNLP (Findings), том EMNLP 2020 из Findings of ACL, стр. 4568-4586. Association for Computational Linguistics, (2020)Characterizing Datapoints via Second-Split Forgetting., , , и . NeurIPS, (2022)Adversarial Robustness Against the Union of Multiple Perturbation Models., , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 6640-6650. PMLR, (2020)Can Neural Network Memorization Be Localized?, , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 23536-23557. PMLR, (2023)Model-tuning Via Prompts Makes NLP Models Adversarially Robust., , , , и . EMNLP, стр. 9266-9286. Association for Computational Linguistics, (2023)Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling., , , , , и . ACL (1), стр. 14044-14072. Association for Computational Linguistics, (2024)Data-Free Model Extraction., , , и . CVPR, стр. 4771-4780. Computer Vision Foundation / IEEE, (2021)