English(EN) High-dimensional learning dynamics of multi-pass Stochastic Gradient Descent in multi-index models

新研究详细介绍了高维模型中多通道SGD的学习动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

一篇新发表在arXiv上的论文详细介绍了高维、非凸多指标模型中多通道随机梯度下降（SGD）的学习动力学。该研究开发了这些动力学的一个渐近精确表征，揭示了一个由泊松跳跃过程驱动的动力学平均场方程控制的连续时间尺度极限。该框架连接了各种现有的SGD理论，并表明与扩散近似或梯度流相比，多通道SGD可能收敛到具有不同统计特性的局部最优解。 AI

影响为理解复杂模型中的SGD优化提供了理论框架，可能影响未来的算法设计。

排序理由发表在arXiv上的学术论文，详细介绍了机器学习动力学方面的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Zhou Fan, Leda Wang · 2026-10-23 04:00

High-dimensional learning dynamics of multi-pass Stochastic Gradient Descent in multi-index models

arXiv:2601.21093v3 Announce Type: replace-cross Abstract: The learning dynamics of Stochastic Gradient Descent (SGD) are believed to play an important role in influencing the generalization properties of trained models. While the precise dynamics of how online (one-pass) SGD opti…

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High-dimensional learning dynamics of multi-pass Stochastic Gradient Descent in multi-index models

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