English(EN) Convergence of stochastic gradient descent for learning linear neural networks

新理论证明了深度线性网络中SGD的收敛性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员在深度线性神经网络的背景下，为随机梯度下降（SGD）建立了新的理论收敛保证。这项工作将现有的梯度下降结果扩展到正则化平方损失下的SGD，证明了SGD迭代的几乎必然有界性。该分析利用随机逼近理论中的渐近伪轨迹框架，在温和的条件下显示了收敛到临界点，而无需Lipschitz性质或白化数据。这代表了弥合深度学习模型理论优化与实际随机训练之间差距的重要一步。 AI

影响为简化深度学习模型中的SGD优化提供了理论基础，可能为更复杂的网络训练提供信息。

排序理由学术论文，详细介绍了深度线性网络中SGD的理论收敛特性。[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) · Gabin Maxime Nguegnang, Bubacarr Bah, Holger Rauhut, Ulrich Terstiege · 2026-10-21 04:00

随机梯度下降用于学习线性神经网络的收敛性

arXiv:2610.28561v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is the cornerstone of modern deep learning, yet its theoretical convergence properties remain poorly understood in non-convex settings. To address this gap, we study SGD dynamics in deep linear neur…

本文如何被排名

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随机梯度下降用于学习线性神经网络的收敛性

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