English(EN) Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method

Adam 优化器获得首次无条件误差分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一个新的理论框架，为 Adam 随机梯度下降优化方法提供统一的先验界和误差分析。这项工作通过首次为 Adam 应用于强凸随机优化问题提供无条件误差分析，解决了长期存在的学术难题。此前的分析是有条件的，假设 Adam 的参数保持均匀有界。 AI

影响为理解和潜在改进 AI 系统中广泛使用的优化算法的性能提供了理论基础。

排序理由学术论文，详细介绍了对优化方法的新理论分析。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Steffen Dereich, Thang Do, Arnulf Jentzen · 2026-09-02 04:00

Adam随机梯度下降优化方法的先验界和误差分析统一

arXiv:2603.18899v2 Announce Type: replace Abstract: The adaptive moment estimation (Adam) optimizer proposed by Kingma & Ba (2014) is presumably the most popular stochastic gradient descent (SGD) optimization method for the training of deep neural networks (DNNs) in artificial in…