English(EN) Eigenvalues of the Hessian in Deep Learning: The Origin of Symmetry and Its Breaking

深度学习Hessian谱由隐藏对称性解释

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

一篇新发表在arXiv上的论文探讨了深度学习模型中Hessian矩阵的谱特性。研究人员观察到，训练模型中的特征值倾向于聚集，一大群接近零，少数是离群值。该研究提出，这些模式源于一种隐藏的、高度对称的参考配置。模型架构、数据或参数度量的修改会打破这种对称性，从而导致观察到的特征值分布。 AI

影响为理解模型行为和潜在的架构改进途径提供了理论框架。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了深度学习的理论发现。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yossi Arjevani · 2026-10-08 04:00

深度学习中Hessian的特征值：对称性及其破缺的起源

arXiv:2610.09919v1 Announce Type: new Abstract: Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk near zero and a few isolated outliers. This paper shows that a natural account of t…