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

新论文分析了关键初始化对神经网络导数的影响

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新论文探讨了宽标量输入网络的关键初始化，重点关注它如何影响更高阶输入导数。研究发现，虽然“混沌边缘”条件可以保持一阶扰动，但它会破坏对物理信息损失和分数匹配至关重要的更高阶导数。该研究推导了均场递归来分析这些效应，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，导数的方差被证明保持有界。 AI

影响 为神经网络初始化提供了理论见解，可能指导未来的模型架构和训练稳定性。

排序理由 该集群包含一篇详细介绍神经网络初始化新研究成果的学术论文。

在 arXiv stat.ML 阅读 →

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新论文分析了关键初始化对神经网络导数的影响

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该集群包含一篇详细介绍神经网络初始化新研究成果的学术论文。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    关键初始化导致宽标量输入网络中的高阶输入导数不稳定

    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 scalar-input fully connected networks, using a joi…

  2. arXiv stat.ML TIER_1 English(EN) · Prashant Singh, Pranav Singh ·

    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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