English(EN) Nonlinear Dynamics In Optimization Landscape of Shallow Neural Networks with Tunable Leaky ReLU

新框架分析浅层神经网络中的非线性动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一个理论框架来分析浅层神经网络优化景观中的非线性动力学。该框架适用于具有四个或更多神经元的网络，通过调整Leaky ReLU激活函数的泄漏参数，使用等变梯度度来识别临界点分岔。研究发现，在临界参数值处发生多模退化，这与神经元数量无关，并且这些分岔与宽度无关，仅发生在非负泄漏参数的情况下。 AI

影响为神经网络的优化提供了理论见解，可能为未来的模型架构和训练策略提供信息。

排序理由学术论文，详细介绍了分析神经网络优化新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Jingzhou Liu · 2026-09-02 04:00

具有可调斜率ReLU的浅层神经网络优化景观中的非线性动力学

arXiv:2510.25060v2 Announce Type: replace-cross Abstract: In this work, we study the nonlinear dynamics of a shallow neural network trained with mean-squared loss and leaky ReLU activation. Under Gaussian inputs and equal layer width k, (1) we establish, based on the equivariant …

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具有可调斜率ReLU的浅层神经网络优化景观中的非线性动力学

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