English(EN) Compatible Likelihoods for Flow Matching on Manifolds

新的流匹配目标改进了流形上的生成模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了一种新的流匹配目标，用于在流形上训练生成模型，解决了现有方法在紧凑空间（如一维平坦环面）上失败的问题。他们提出的方法基于端点的指数族构建，是兼容的，并确保速度在紧凑流形上可行。在经验上，这种方法在地理空间基准测试中显示出改进的负对数似然，并在人体姿态数据集上优于先前的工作。 AI

影响这项研究可能导致在地理空间分析和人体姿态估计等领域的复杂数据分布方面，生成模型更加准确和高效。

排序理由这是一篇发表在arXiv上的研究论文，详细介绍了一种新的生成模型方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Lucas Ng, Georgios Batzolis, Mark Girolami · 2026-10-26 04:00

Manifold 上的流匹配的兼容似然

arXiv:2610.31981v1 Announce Type: cross Abstract: On the simplest non-Euclidean manifold, the 1D flat torus, we exhibit a distribution where a recently proposed extension of Riemannian flow matching is globally optimized by a velocity field that pushes samples away from the data …