English(EN) Local Stability and Gaussian Smoothing of Quantized Neural Networks

新方法使用高斯平滑处理量化神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-23 04:00

研究人员开发了一种使用高斯平均作为量化神经网络平滑近似的方法。该技术在有界局部振荡下应用时，为原始函数与其平滑版本之间的差异提供了依赖于维度的界限。该研究还为ReLU和符号函数等常见激活函数提供了闭式高斯平均，并展示了它们在高维二元感知器模型上的应用。 AI

影响引入了一个新颖的理论框架，用于分析和潜在地改进量化神经网络的稳定性和训练。

排序理由详细介绍一种新的神经网络分析理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Sergey Salishev, Anton Makarov, Oleg Granichin · 2026-07-23 04:00

量化神经网络的局部稳定性和高斯平滑

arXiv:2607.20153v1 Announce Type: new Abstract: We study Gaussian averaging as a smooth surrogate for quantized neural models. Under bounded local oscillation, we derive a local dimension-dependent bound on |f-g|, linking Gaussian smoothing to the stability analysis of discontinu…