English(EN) How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning

新原理为使用 StopGrads 的机器学习模型训练提供了理论基础

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

一篇新论文引入了“stopgrad 回归原理”，为使用 stopgrads 训练机器学习模型提供了理论基础。该原理表征了各种 stopgrad 目标的平稳点和收敛保证，包括流图、强化学习和扩散采样器中使用的目标。研究表明，对于流图目标，唯一的平稳点是真实的流图，并提出了将训练内存使用量减少一半的修改。 AI

影响为优化机器学习模型提供了理论框架，有可能提高训练效率和稳定性。

排序理由该集群包含一篇详细介绍机器学习模型训练新理论原理的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Max W. Shen, Mark Goldstein, Zichu Wang, Aahlad Puli, Rajesh Ranganath · 2026-09-16 04:00

我如何学会停止担忧并拥抱StopGrads：平稳性、收敛性以及在流图学习上的案例研究

arXiv:2609.16222v1 Announce Type: new Abstract: Stopgrads are widely used in training machine learning models, but stopgrads can alter the gradient, stationary points and convergence guarantees of the original objective, which can make stopgrad training theoretically ungrounded. …

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我如何学会停止担忧并拥抱StopGrads：平稳性、收敛性以及在流图学习上的案例研究

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