English(EN) The role of parameter Jacobians in the stability of network outputs

新研究探索参数雅可比矩阵在神经网络稳定性中的作用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

本文在学习模型和神经切线核（NTK）的框架下，探讨了网络输出的稳定性。研究表明，线性化动力学自然地导出了半群公式，通过希尔伯特空间上线性算子的特殊半群来呈现时间动力学。该研究提供了新的、明确的先验扰动界限结果，包括NTK构造的有限时间扰动估计以及对非自治NTK演化的扩展。 AI

排序理由该条目是发表在arXiv上的学术论文，详细介绍了数学研究。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv stat.ML TIER_1 English(EN) · Halyun Jeong, Palle E. T. Jorgensen, Hyun-Kyoung Kwon, Myung-Sin Song, James Tian · 2026-08-31 04:00

参数雅可比矩阵在网络输出稳定性中的作用

arXiv:2608.27748v1 Announce Type: cross Abstract: In the framework of network dynamics, learning models, and neural tangent kernels (NTK), we show that the corresponding linearized dynamics leads naturally to a semigroup formulation. More precisely, in our analysis of input/outpu…