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

新研究详解基于分数的生成模型SGD收敛性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员发表了一篇论文，详细介绍了随机梯度下降（SGD）应用于基于分数的生成模型（SGMs）时的非渐近收敛性。该研究为训练SGMs的SGD提供了理论保证，解决了优化动力学问题，而这方面的研究比其采样过程的研究要少。该工作为一般分数参数化建立了收敛率，并使用神经切线核分析了过参数化网络，为实际中的权重选择提供了指导。 AI

影响 为优化基于分数的生成模型提供了理论指导，可能提高其训练效率和性能。

排序理由 该集群包含一篇关于机器学习主题的新发表的学术论文。

在 arXiv stat.ML 阅读 →

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新研究详解基于分数的生成模型SGD收敛性

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Stanislas Strasman (SU, LPSM), Sobihan Surendran (SU, LPSM), Sylvain Le Corff (SU, LPSM) ·

    基于分数的生成模型中随机梯度下降的非渐近收敛性

    arXiv:2607.04775v1 Announce Type: new Abstract: 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 op…

  2. arXiv stat.ML TIER_1 English(EN) · Sylvain Le Corff ·

    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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