arXiv Machine Learning
Stochastic Flow Map for Count Data
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arXiv Machine Learning
Sep 23
Flow Matching for Count Data
Flow Matching for Count Data introduces count‑FM, a flow‑matching framework tailored to high‑dimensional count data such as single‑cell RNA sequencing and neural spike trains. The method models transitions with a continuous‑time birth‑death process that uses local unit jumps, enabling efficient, simulation‑free learning of conditional transition rates directly in count space. Experiments show that count‑FM variants achieve strong sample quality with fewer parameters and provide interpretable transport paths for tasks including unconditional generation, transport, and conditional generation on real biological datasets.
arXiv Machine Learning
5d ago
CRNDiff: Count-Native Diffusion Framework via Chemical Reaction Networks
CRNDiff is a new count‑native diffusion framework that uses stochastic chemical reaction networks to model nonnegative integer data such as single‑cell RNA sequencing. It provides a closed‑form forward‑noising kernel, enabling efficient reverse sampling via forward‑filtering backward‑sampling and data‑driven selection of the terminal noising time. The method also introduces tilted Feynman–Kac steering to sample rare subpopulations without retraining, and demonstrates superior conditional fidelity and marker‑level preservation on human heart scRNA‑seq data.
arXiv Machine Learning
Jul 9
Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data
arXiv:2607. 06583v1 Announce Type: cross Abstract: DNA methylation (DNAm) serves as one of the most robust molecular biomarkers of biological aging.
arXiv AI
Jun 8
CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
arXiv:2604. 03779v2 Announce Type: replace-cross Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped.
arXiv AI
Jun 11
Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
arXiv:2605. 00545v2 Announce Type: replace-cross Abstract: Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis.
arXiv AI
Jun 2
Variational Learning for Insertion-based Generation
arXiv:2606. 02133v1 Announce Type: cross Abstract: Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generated in non-fixed and prescribed orders.