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English(EN) Multi-marginal temporal Schr\"odinger Bridge Matching from unpaired data

新方法增强了从静态数据中重建时间动态

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了从静态数据中重建时间动态的新方法，解决了现有方法的局限性。多边际时间薛定谔桥匹配（MMtSBM）算法通过实现可扩展的高维分析，并在转录组轨迹推断等领域取得最先进的性能，扩展了先前的工作。此外，扭曲薛定谔桥匹配（TSBM）为广义薛定谔桥问题提供了一种新颖的方法，通过引入势能来改进复杂场景（如人群导航和单细胞数据）中的轨迹推断。 AI

影响 时间动态重建方面的这些进展可以改进生物学和导航等领域的复杂系统建模。

排序理由 两篇arXiv论文介绍了使用薛定谔桥匹配进行时间动态重建的新方法。

在 arXiv cs.LG 阅读 →

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新方法增强了从静态数据中重建时间动态

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两篇arXiv论文介绍了使用薛定谔桥匹配进行时间动态重建的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Thomas Gravier, Thomas Boyer, Auguste Genovesio ·

    多边际时间薛定谔桥匹配来自非配对数据

    arXiv:2510.01894v3 Announce Type: replace Abstract: Many natural dynamic processes -- such as in vivo cellular differentiation or disease progression -- can only be observed through the lens of static sample snapshots. While challenging, reconstructing their temporal evolution to…

  2. arXiv stat.ML TIER_1 Deutsch(DE) · Maxence Noble, Marie Scheid, Yazid Janati, Eric Moulines, Alain Durmus ·

    扭曲的薛定谔桥匹配

    arXiv:2607.16987v1 Announce Type: new Abstract: Over the past few years, diffusion-based Schr\"odinger bridge models have been proposed to approximate optimal transport dynamics between two prescribed boundary distributions, with successful applications to generative modeling. Mo…

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