Mar 3, 2021 · Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations.
Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations. 1 Introduction.
Oct 25, 2020 · In this paper, we provide the first formal analysis for feed-forward neural networks with non-negative monotone activation functions against norm-bounded weight ...
This paper provides the first integral study and analysis for feed-forward neural networks in terms of the robustness in pairwise class margin and its ...
Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations. ResearchGate Logo.
Joint training with the two theory-driven terms in (2) indeed yields more generalizable and robust neural networks against weight perturbations. We trained ...
Jun 10, 2024 · Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations.
Dec 17, 2021 · Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations. 1 ...
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Dec 3, 2021 · Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations - Characterizing neural network generalization ...
Formalizing generalization and robustness of neural networks to weight perturbations. YL Tsai, CY Hsu, CM Yu, PY Chen. arXiv preprint arXiv:2103.02200, 2021.