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English(EN) Koopman-informed recurrent neural networks

新型RNN利用Koopman算子理论绕过梯度下降

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了基于Koopman的循环神经网络（RNN），它们可以绕过传统的基于梯度的训练方法，如时间反向传播。这种新颖的方法将随机特征网络与Koopman算子理论相结合，无需梯度即可构建所有RNN的权重和偏差。该方法在时间序列分析、混沌动力学系统和控制问题方面，已证明其预测精度与标准模型相当，但训练时间显著缩短。 AI

影响 这项研究为循环神经网络提供了更快、更稳定的训练途径，尤其适用于复杂的动力学系统。

排序理由 该集群包含一篇详细介绍新型神经网络训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新型RNN利用Koopman算子理论绕过梯度下降

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该集群包含一篇详细介绍新型神经网络训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Erik Lien Bolager, Ana \v{C}ukarska, Iryna Burak, Zahra Monfared, Felix Dietrich ·

    Koopman-informed recurrent neural networks

    arXiv:2410.23467v3 Announce Type: replace Abstract: Recurrent neural networks are a successful neural architecture for many time-dependent problems, including time series analysis, forecasting, and modeling of dynamical systems. In the context of dynamical systems, training with …

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