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新的RGFM生成模型使用重整化群流实现可扩展的局部数据生成

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了重整化群流匹配（RGFM），一个新颖的生成建模框架，解决了计算成本与捕捉长程关联能力之间的权衡问题。RGFM通过使用精确的重整化群流作为概率路径，在不同空间尺度上构建数据生成过程，逐步从长波长结构生成到短波长结构。这种方法实现了局部生成建模，其计算成本几乎与系统体积呈线性关系，在生成连贯且高质量的样本方面优于传统的局部流匹配，尤其是在FFHQ等图像数据集上。 AI

影响 这种新的RGFM方法能够实现更高效、更连贯的复杂数据生成，可能影响依赖于高质量合成数据的领域。

排序理由 该集群包含一篇详细介绍新生成建模技术的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新的RGFM生成模型使用重整化群流实现可扩展的局部数据生成

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Kanta Masuki, Yuto Ashida ·

    可扩展局部生成模型中的重整化群流匹配

    arXiv:2608.23696v1 Announce Type: new Abstract: Despite their remarkable success in modeling complex data, generative models face a fundamental tradeoff. Global approaches can capture full structural coherence but suffer from high computational costs, while local models are effic…

  2. arXiv stat.ML TIER_1 English(EN) · Moritz Piening, Christian Wald ·

    用于等变图生成的 Gromov-Monge 流匹配

    arXiv:2608.26961v1 Announce Type: cross Abstract: Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in generative models. In flow matching, however, symmetry may also enter the source--target coupling: once graph pairs are c…

  3. Towards AI TIER_1 English(EN) · Phrugsa Limbunlom (Gift) ·

    使用流匹配、最优传输和薛定谔桥进行生成式建模

    <h3>Generative Modeling with Flow Matching, Optimal Transport, and Schrödinger bridge</h3><h4>A unified formulation of flow matching, optimal transport, and the Schrödinger bridge as transport between distributions.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max…

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