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arXiv Machine Learning By Qiangwei Peng, Lezhi Chen, Peijie Zhou

Multiscale Supervised Unbalanced Optimal Transport Flow Matching

Read the original on arXiv Machine Learning →

Multiscale Supervised Unbalanced Optimal Transport Flow Matching (MUST‑FM) is a simulation‑free framework that scales unbalanced optimal transport (UOT) for single‑cell trajectory inference by exploiting hierarchical data structure. It optionally incorporates supervised transition priors, such as cell lineages, to guide displacement fields and mass variations. Experiments demonstrate reduced computational overhead and robust, biologically meaningful trajectory inference on atlas‑scale single‑cell datasets.

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