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English(EN) Symmetry without a manifold: intrinsic dimension on orbits

新研究挑战标准神经缩放指数推导

一篇新研究论文提出了一种替代标准神经缩放指数的几何推导方法,该方法通常依赖于数据流形的内在维度。作者们证明,对于 $\mathbb{Z}_p$ 中的模加法,这种标准方法是未定义的,因为精确的代数解涉及作用于等距变换的 $\mathbb{Z}_p$ 轨道。相反,他们表明这种关系遵循与网络隐藏宽度相关的指数曲线,具有很高的 R 方值,表明该模型能更好地捕捉观察到的行为。 AI

影响 提出了一种理解神经缩放的新理论框架,可能影响模型设计和分析。

排序理由 该集群包含一篇详细介绍神经缩放指数新理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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

新研究挑战标准神经缩放指数推导

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该集群包含一篇详细介绍神经缩放指数新理论方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Chon-Fai Kam, Miloud Bessafi, Fr\'ed\'eric Cadet ·

    对称性无流形:轨道上的内禀维度

    arXiv:2609.17926v1 Announce Type: cross Abstract: The standard geometric derivation of neural scaling exponents takes the intrinsic dimension of a data manifold as its input. On modular addition in $\mathbb{Z}_p$ that derivation has no input. The exact algebraic solution is an or…