English(EN) High-dimensional Limit of SGD for Diagonal Linear Networks

论文分析了SGD在高维线性网络中的动力学行为

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-16 22:26

一篇新论文详细介绍了随机梯度下降（SGD）在对角线性网络中的高维行为。研究表明，在高维情况下，SGD的动力学行为可以用随机微分方程精确建模。这使得可以推导出一个确定性偏微分方程，该方程跟踪风险和曲率等关键统计数据，最终证明了指数级收敛到零风险。 AI

影响为神经网络组件的优化提供了理论见解，可能为未来的模型训练策略提供信息。

排序理由在arXiv上发表的学术论文，详细介绍了机器学习优化方法的理论分析。

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arXiv stat.ML TIER_1 English(EN) · Bego\~na Garc\'ia Malaxechebarr\'ia, Courtney Paquette, Maryam Fazel, Dmitriy Drusvyatskiy · 2026-05-19 04:00

SGD在高维对角线性网络中的极限

arXiv:2605.17177v1 Announce Type: cross Abstract: Understanding the behavior of stochastic gradient methods is a central problem in modern machine learning. Recent work has highlighted diagonal linear networks as a simplified yet expressive setting for analyzing the optimization …
arXiv stat.ML TIER_1 English(EN) · Dmitriy Drusvyatskiy · 2026-05-16 22:26

SGD在高维线性对角网络的极限

Understanding the behavior of stochastic gradient methods is a central problem in modern machine learning. Recent work has highlighted diagonal linear networks as a simplified yet expressive setting for analyzing the optimization and generalization properties of neural models. In…