English(EN) Critical initialization destabilizes higher input derivatives in wide scalar-input networks

新研究揭示关键初始化破坏神经网络中更高阶导数

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员发现，关键初始化会破坏具有标量输入的宽神经网络中更高阶输入导数的稳定性。虽然边缘混沌条件可以保持一阶扰动，但依赖于更高阶导数的损失和正则化会受到影响。该研究推导了在方差固定点下精确的均场递归，表明当激活函数具有非零曲率时，二阶导数方差随深度线性增长。对于残差网络，已证明所有有限阶导数都保持均匀有界方差。 AI

影响这项研究为神经网络初始化提供了理论见解，可能影响未来模型的设计和训练。

排序理由该条目是发表在arXiv上的学术论文，详细介绍了关于神经网络初始化的理论发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Prashant Singh, Pranav Singh · 2026-09-10 04:00

关键初始化导致宽标量输入网络中更高阶输入导数失稳

arXiv:2609.09244v1 Announce Type: new Abstract: The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matching and derivative regularization depend on higher input derivatives. For smooth sc…

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报道来源 [1]

关键初始化导致宽标量输入网络中更高阶输入导数失稳

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