English(EN) Understanding Deep Learning via Notions of Rank

基于秩的泛化分析推动深度学习理论发展 · arXiv 研究

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

一篇新论文提出，秩的概念是理解深度学习的基础，尤其是在泛化和表达能力方面。研究表明，基于梯度的训练可以隐式地将神经网络正则化至较低的秩，这可能解释了其在图像和文本等自然数据上的泛化能力。该研究还利用量子物理学中的秩概念来表征图神经网络的建模能力，并提出了对设计正则化方案和数据预处理的实际意义。 AI

影响提出了一个理论框架，可能带来改进的深度学习模型设计和数据预处理技术。

排序理由学术论文发表在arXiv上，详细介绍了深度学习的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

Noam Razin

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

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Newsworthiness bucket: Tool; 学术论文发表在arXiv上，详细介绍了深度学习的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Noam Razin · 2026-09-01 04:00

通过秩的概念理解深度学习

arXiv:2408.02111v4 Announce Type: replace-cross Abstract: Despite the extreme popularity of deep learning in science and industry, its formal understanding is limited. This thesis puts forth notions of rank as key for developing a theory of deep learning, focusing on the fundamen…

本文如何被排名

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通过秩的概念理解深度学习

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