English(EN) Probability-Signature Dynamics: Unpacking Modular Addition Learning Within Two-Layer Networks

新方法解释神经网络如何学习模块加法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种使用概率签名的新方法，以理解神经网络如何学习模块加法任务。该方法揭示了这些网络会形成特定的傅里叶结构表示，从而实现精确泛化。该研究还解释了为什么在没有连贯泛化规则的情况下，噪声数据有时会导致这些网络在初期学习更快。 AI

影响为理解和潜在改进神经网络在特定任务上的学习提供了理论框架。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了一种理解神经网络学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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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arXiv cs.AI TIER_1 English(EN) · Yunji Wang, Junjie Yao, Linyu Liu, Pinyan Lu, Zhi-Qin John Xu · 2026-10-09 04:00

概率签名动力学：解析双层网络中的模块化加法学习

arXiv:2610.11833v1 Announce Type: new Abstract: Neural networks trained on modular addition tasks often develop Fourier-structured representations that support exact generalization. While prior work has identified these Fourier circuits, the mechanism by which gradient-based trai…