English(EN) Finite-width shift of the edge of chaos in deep neural networks

新的Crinit初始化规则改进了深度神经网络的训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一种名为Crinit的新型深度神经网络初始化规则，该规则能精确地将其置于有限宽度的混沌边缘。该规则源于将输入之间的距离分析为随机游走，并考虑了有限宽度下混沌边缘的偏移。在MNIST和Fashion-MNIST数据集上的模拟和实验表明，Crinit的性能优于标准的初始化规则，尤其是在其他方法经常失败的更深层网络中，从而实现了更准确的训练和更少的步骤。 AI

影响为深度学习模型提供了更鲁棒的初始化策略，有可能提高性能和训练效率。

排序理由详细介绍深度神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Arn Caesar · 2026-11-05 05:00

深度神经网络中混沌边缘的有限宽度偏移

arXiv:2611.06582v1 Announce Type: cross Abstract: Deep neural networks train best when they are initialised at the edge of chaos, the critical line in the plane of weight and bias variances on which the distance between two inputs neither grows nor shrinks with depth. The line is…

本文如何被排名

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深度神经网络中混沌边缘的有限宽度偏移

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