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Rethinking softmax cross-entropy loss for adversarial robustness

T. "Pang, K. "Xu, Y. "Dong, C. "Du, N. "Chen, and J. "Zhu. (2019)

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Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks.T. Pang, K. Xu, and J. Zhu. ICLR, OpenReview.net, (2020)Improving Adversarial Robustness via Promoting Ensemble Diversity.T. Pang, K. Xu, C. Du, N. Chen, and J. Zhu. ICML, volume 97 of Proceedings of Machine Learning Research, page 4970-4979. PMLR, (2019)Max-Mahalanobis Linear Discriminant Analysis Networks.T. Pang, C. Du, and J. Zhu. ICML, volume 80 of Proceedings of Machine Learning Research, page 4013-4022. PMLR, (2018)Efficient Learning of Generative Models via Finite-Difference Score Matching.T. Pang, T. Xu, C. Li, Y. Song, S. Ermon, and J. Zhu. NeurIPS, (2020)Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows.C. Du, T. Li, T. Pang, S. Yan, and M. Lin. ICML, volume 202 of Proceedings of Machine Learning Research, page 8565-8584. PMLR, (2023)Crafting Heavy-Tails in Weight Matrix Spectrum without Gradient Noise.V. Kothapalli, T. Pang, S. Deng, Z. Liu, and Y. Yang. CoRR, (2024)Finetuning Text-to-Image Diffusion Models for Fairness.X. Shen, C. Du, T. Pang, M. Lin, Y. Wong, and M. Kankanhalli. CoRR, (2023)Weak-to-Strong Jailbreaking on Large Language Models.X. Zhao, X. Yang, T. Pang, C. Du, L. Li, Y. Wang, and W. Wang. CoRR, (2024)Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast.X. Gu, X. Zheng, T. Pang, C. Du, Q. Liu, Y. Wang, J. Jiang, and M. Lin. CoRR, (2024)Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart.T. Pang, H. Zhang, D. He, Y. Dong, H. Su, W. Chen, J. Zhu, and T. Liu. CVPR, page 15202-15212. IEEE, (2022)

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Rethinking softmax cross-entropy loss for adversarial robustness

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Rethinking softmax cross-entropy loss for adversarial robustness

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Rethinking softmax cross-entropy loss for adversarial robustness