English(EN) Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

论文分析基于分数的生成模型的SGD收敛性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-06 08:07

研究人员发表了一篇论文，分析了基于分数的生成模型（SGMs）训练的优化动力学。该研究侧重于随机梯度下降（SGD），并为一般的分数参数化提供了收敛速率，同时考虑了权重因子。此外，对于过参数化的双层ReLU网络，该论文使用神经切线核（Neural Tangent Kernel）分析来确定SGD轨迹上的分数近似误差界限。研究结果为重排因子在SGMs分数近似中的影响提供了理论指导。 AI

影响为优化基于分数的生成模型提供了理论指导，可能提高训练效率和性能。

排序理由该集群包含一篇详细阐述机器学习训练方法的理论分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-06 08:07

基于分数的生成模型中随机梯度下降的非渐近收敛性

Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling procedures are increasingly well understood, the optimization dynamics underlying their training re…

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基于分数的生成模型中随机梯度下降的非渐近收敛性

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