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English(EN) Broken Ergodicity and the Violation of the Fluctuation-Dissipation Theorem Lead to Generalization Beyond Overfitting in Machine Learning

机器学习泛化与超导转变物理学相关联

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员利用动力学平均场理论解释了机器学习中的“双下降”现象，即模型容量超过数据点时泛化能力仍会提高。这种行为被确定为训练过程中的一个相变，其特征是由于ergodicity破坏导致涨落耗散定理的失效。该研究的发现表明，该相变的响应函数与超导转变的伦敦模型之间存在联系，波函数刚度与神经网络的泛化能力相关。 AI

影响 为理解和潜在地改进大型神经网络的泛化能力提供了理论框架。

排序理由 学术论文，详细介绍了机器学习泛化的新颖理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

机器学习泛化与超导转变物理学相关联

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学术论文，详细介绍了机器学习泛化的新颖理论框架。[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) · Chan Li, Nigel Goldenfeld ·

    不可靠的遍历性与涨落-耗散定理的违反导致机器学习中超越过拟合的泛化

    arXiv:2607.04135v1 Announce Type: cross Abstract: The remarkable ability of modern neural networks to generalize improves with increasing network capacity, even when the number of model parameters or effective degrees of freedom exceeds the number of training data points. This ph…

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