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New Adam Theorem Unveiled for Spectral Heavy-Tail Onset in ML Models

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为“完整Adam定理”的综合理论框架，用于分析高斯Stein-Hermite师生模型中的谱重尾Onset。该定理详细阐述了Adam优化算法的每一步，从动量和分母计算到投影更新响应和近似目标KL收缩的推导。研究结果建立了一个依赖于谱隙的命中时间定律，并证明了对于该模型而言，不可能存在更强的任意梯度Adam定理。 AI

影响 提供了对机器学习模型优化动力学的更深层次的理论理解。

排序理由 该条目是发表在arXiv上的研究论文，详细介绍了一个新的理论定理。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New Adam Theorem Unveiled for Spectral Heavy-Tail Onset in ML Models

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该条目是发表在arXiv上的研究论文，详细介绍了一个新的理论定理。[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) · Zongmin Liu ·

    谱重尾 onset 的完整 Adam 定理

    arXiv:2609.12996v1 Announce Type: new Abstract: We prove a full Adam theorem for spectral heavy-tail onset in a closed Gaussian Stein-Hermite teacher-student state-evolution model. The theorem begins with the actual full-batch Adam recurrences, derives the population gradient by …

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