English(EN) Diffusion Score Estimation with Shallow Neural Networks: Regularity, Approximation, and Finite-Sample Rates

新研究探索使用浅层神经网络进行扩散评分估计

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

研究人员开发了一种使用浅层神经网络估计扩散评分的方法，特别关注浅层ReLU网络。该研究为估计过程提供了样本复杂度和逼近误差的理论界限。这些发现对反向过程采样具有启示作用，能够对逼近误差进行显式控制，并提供足够的路径范数预算以提高采样精度。 AI

影响这项研究为扩散模型中的评分估计提供了理论基础，有可能改进采样技术。

排序理由该集群包含一篇详细介绍机器学习理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Fan Liu, Yahong Yang, Wei Zhu · 2026-10-21 04:00

基于浅层神经网络的扩散分数估计：正则性、近似与有限样本率

arXiv:2610.27206v1 Announce Type: cross Abstract: We study the sample complexity of estimating Ornstein--Uhlenbeck diffusion scores in $\mathbb R^d$ using denoising score matching (DSM) with finite-width shallow ReLU networks, and its implications for reverse-process sampling. Un…