English(EN) High-Dimensional Analysis of Gradient Flow for Extensive-Width Quadratic Neural Networks

新研究分析高维神经网络的训练动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-09 04:00

一篇新发表在arXiv上的研究论文分析了具有二次激活函数的浅层神经网络在高维训练动力学。该研究侧重于网络宽度随输入维度缩放的广泛宽度区域，并使用动力学平均场理论来表征梯度流。研究结果为过参数化如何影响学习和泛化提供了量化见解，揭示了在存在标签噪声的情况下出现的双下降现象，并为l2正则化下的完美恢复阈值提供了精确表达式。 AI

影响为理解过参数化对神经网络学习和泛化的影响提供了理论基础。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了神经网络动力学的理论分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Simon Martin (DI-ENS, LPENS, SIERRA), Giulio Biroli (LPENS), Francis Bach (DI-ENS, SIERRA) · 2026-07-09 04:00

高维二次神经网络广泛宽度梯度流分析

arXiv:2601.10483v2 Announce Type: replace-cross Abstract: We study the high-dimensional training dynamics of a shallow neural network with quadratic activation in a teacher-student setup. We focus on the extensive-width regime, where the teacher and student network widths scale p…

本文如何被排名

报道来源 [1]

高维二次神经网络广泛宽度梯度流分析

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