具有迭代参考精炼的多边际薛定谔桥
Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
AI总结：
提出一种迭代参考精炼的多边际薛定谔桥方法，从多时间点样本快照中推断未观测群体轨迹，仅需指定参考动态族而非固定动态，克服了现有方法仅限两点插值或依赖固定参考的局限。
摘要：
从业者通常旨在利用多个时间点的样本快照来推断未观测到的群体轨迹。例如，给定单细胞测序数据，科学家希望了解基因表达在细胞生命周期中是如何变化的。但是对任何细胞进行测序都会破坏该细胞。因此我们只能在单一时间点获取特定细胞的数据，但我们拥有许多细胞的数据。深度学习社区最近探索了在类似场景中使用薛定谔桥（SBs）及其扩展。然而，现有方法要么（1）仅在两个时间点之间进行插值，要么（2）需要单一的固定参考动态（在SBs中通常设为布朗运动）。但是从相邻时间点分段学习可能无法捕捉长期依赖关系。而且从业者通常能够为参考动态指定一个模型族，但无法指定其中的确切参数值。因此我们提出了一种新方法，该方法（1）从多个时间点的样本快照中学习未观测到的轨迹，并且（2）仅需要指定一族参考动态，而非单一固定的参考动态。我们在模拟数据和真实数据上展示了我们方法的优势。
英文摘要：
Practitioners often aim to infer an unobserved population trajectory using sample snapshots at multiple time points. E.g., given single-cell sequencing data, scientists would like to learn how gene expression changes over a cell's life cycle. But sequencing any cell destroys that cell. So we can access data for any particular cell only at a single time point, but we have data across many cells. The deep learning community has recently explored using Schrödinger bridges (SBs) and their extensions in similar settings. However, existing methods either (1) interpolate between just two time points or (2) require a single fixed reference dynamic (often set to Brownian motion within SBs). But learning piecewise from adjacent time points can fail to capture long-term dependencies. And practitioners are typically able to specify a model family for the reference dynamic but not the exact values of the parameters within it. So we propose a new method that (1) learns the unobserved trajectories from sample snapshots across multiple time points and (2) requires specification only of a family of reference dynamics, not a single fixed one. We demonstrate the advantages of our method on simulated and real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
