Flow Matching for Count Data
High-dimensional count data arise in applications such as single-cell RNA sequencing and neural spike trains, where mappings between distributions across successive batches or time points form critical components of data analysis. The recent success of diffusion- and flow-based deep generative models for images, video, and text motivates extending these ideas to count-valued settings, but many existing methods either treat each count as a categorical state or transform counts into a continuous space, neither of which is natural or efficient when the count range is large. We propose count-FM, a flow-matching framework for count data based on a continuous-time birth-death process with local...
| Authors | Ganchao Wei, John Pearson |
|---|---|
| Primary category | stat.ML |
| Published | 8 May 2026 |
| Last updated at source | 22 September 2026 |
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