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English(EN) Physics-Inspired Optimizers beyond the Edge of Stability: Period-Two Orbits and Delayed Instability

物理启发的优化器在稳定性极限之外展现周期二轨道

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员探索了用于深度学习的物理启发的优化器，重点关注它们在稳定性极限之外的行为。他们识别出周期二轨道，如果优化器的弹性在特定范围内，则可以维持稳定性。研究还发现，延迟不稳定性，类似于Neishtadt的慢扫描分岔理论，可以准确描述神经网络中的动力学。在MLP和ResNet上的实验支持了这些理论发现，表明匹配有效学习率可以复制一些观察到的好处。 AI

影响 为深度学习优化动力学提供了理论见解，可能导致更稳定和更高效的训练方法。

排序理由 详细介绍优化算法理论和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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物理启发的优化器在稳定性极限之外展现周期二轨道

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详细介绍优化算法理论和实验结果的学术论文。[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) · Alexander Itin ·

    超越稳定性边缘的受物理启发的优化器：周期二轨道与延迟不稳定性

    arXiv:2610.27321v1 Announce Type: cross Abstract: Gradient descent with momentum can be viewed as a particle moving with friction in the loss landscape. In deep learning the learning rate is usually large, so training is a discrete dynamical system: in many full-batch experiments…

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