arXiv:2509.15368cs.LG2025-09被引 1
提出随机采样方法评估神经网络局部连续性，助力鲁棒性与公平性分析。
Stochastic Sample Approximations of (Local) Moduli of Continuity
- 基于广义导数重构局部连续性模量的非均匀随机采样法
- 可有效评估神经网络在闭环系统中的鲁棒性与公平性表现
- 适合关注模型稳定性和公平性的研究人员
局部连续性模量用于评估神经网络的鲁棒性以及其在闭环模型中重复使用时的公平性。本文重新审视广义导数与局部连续性模量之间的联系，提出一种非均匀随机样本近似方法，用于估计局部连续性模量。该方法对研究神经网络的鲁棒性及重复使用中的公平性具有重要意义。
原文摘要 · Abstract (English)
Modulus of local continuity is used to evaluate the robustness of neural networks and fairness of their repeated uses in closed-loop models. Here, we revisit a connection between generalized derivatives and moduli of local continuity, and present a non-uniform stochastic sample approximation for moduli of local continuity. This is of importance in studying robustness of neural networks and fairness of their repeated uses.
神经网络鲁棒性连续性模量
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