2024/06/07 by Floor Eijkelboom, Eijkelboom, Floor, Grigory Bartosh +8 · 1 voice · 28 citations
Computer Science · Mathematics · #Artificial Intelligence in Games #Combinatorics #Computer science #Data Stream Mining Techniques #Flow (mathematics) #Geometry #Graph #Matching (statistics) #Mathematical optimization #Mathematics #Reinforcement Learning in Robotics #Statistics
paper · pdf · doi:10.48550/arxiv.2406.04843
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a formulation of flow matching as variational inference, which we refer to as variational flow matching (VFM). Based on this formulation we develop CatFlow, a flow matching method for categorical data. CatFlow is easy to implement, computationally efficient, and achieves strong results on graph generation tasks. In VFM, the objective is to approximate the posterior probability path, which is a distribution over possible end points of a trajectory. We show that VFM admits both the CatFlow objective and the original flow matching objective as special cases. We also relate VFM to score-based models, in which the dynamics are stochastic rather than deterministic, and derive a bound on the model likelihood based on a reweighted VFM objective. We evaluate CatFlow on one abstract graph generation task and two molecular generation tasks. In all cases, CatFlow exceeds or matches performance of the current state-of-the-art models.