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English(EN) AURA: Angular Update Rate Adaptation for training complex-valued neural networks

新的AURA方法增强了复值神经网络的训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AURA（角度更新率自适应），一种用于训练复值神经网络（CVNNs）的新颖方法。AURA通过整合复平面中的角度信息来增强现有的二阶优化器，在无需额外梯度计算的情况下提高了收敛性。该方法通过将其与Adam和Muon优化器结合，并在各种复值数据任务上进行测试，在大多数情况下均显示出性能提升。 AI

影响 这种新的训练适应方法有望提高复值神经网络在各种应用中的效率和收敛性。

排序理由 该集群包含一篇详细介绍神经网络训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的AURA方法增强了复值神经网络的训练

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Enrico Ballini, Allan Peter Engsig-Karup, Tito Andriollo ·

    AURA：用于训练复值神经网络的角更新率自适应

    arXiv:2609.25914v2 Announce Type: replace Abstract: Complex-valued neural networks (CVNNs) are increasingly adopted for complex-valued data; however, they are often trained with first-order optimizers inherited from the real-valued case. The efficiency of these methods depends la…

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