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English(EN) Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

深度学习理论：神经网络作为计算模型

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新论文探讨了深度学习的理论基础，提出神经网络不仅应被视为函数逼近器，还应被视为计算模型。由 Anastasis Kratsios 撰写的这项研究表明，神经网络可以模拟复杂的数值算法，如牛顿法。该工作确立了神经网络的复杂性不仅受正则性影响，还受算法复杂性的影响，为各种函数类提供了通用逼近保证。 AI

影响 这项研究重塑了对神经网络能力的理解，可能影响未来的模型架构和理论分析。

排序理由 该集群包含一篇详细介绍深度学习理论进展的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

深度学习理论：神经网络作为计算模型

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该集群包含一篇详细介绍深度学习理论进展的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Anastasis Kratsios, Simone Brugiapaglia, Bum Jun Kim, Gregory Cousins, Haitz S\'aez de Oc\'ariz Borde ·

    深度学习的算法基础：复杂性理论速率与通用逼近的表征

    arXiv:2606.26705v1 Announce Type: cross Abstract: Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily captures complexity through regularity, and therefore doe…

  2. arXiv cs.LG TIER_1 English(EN) · Haitz Sáez de Ocáriz Borde ·

    深度学习的算法基础：复杂性理论速率与通用逼近的表征

    Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily captures complexity through regularity, and therefore does not distinguish intuitively simple and complicat…

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