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English(EN) Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models

研究论文详述扩散模型中常微分方程求解器的快速收敛

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了高阶常微分方程（ODE）求解器应用于扩散概率模型时的收敛特性。该研究由郑江林（Zhengjiang Lin）撰写，介绍了并分析了 $p$ 阶龙格-库塔（Runge-Kutta）方案，并在假设分数函数导数有界的情况下证明了其有效性。研究结果表明，生成分布与目标分布之间的总变差距离可以被有效界定，数值实验支持了这些方法的实际应用性。 AI

影响 为提高扩散模型中样本生成质量提供了理论基础。

排序理由 关于扩散概率模型和常微分方程求解器的学术论文。[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 →

研究论文详述扩散模型中常微分方程求解器的快速收敛

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关于扩散概率模型和常微分方程求解器的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin ·

    Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models

    arXiv:2506.13061v4 Announce Type: replace Abstract: Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. A key development is the reformulation of the reverse sampling process as a deterministic probabil…

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