@inproceedings{
eijkelboom2024variational,
title={Variational Flow Matching for Graph Generation},
author={Floor Eijkelboom and Grigory Bartosh and Christian A. Naesseth and Max Welling and Jan-Willem van de Meent},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=UahrHR5HQh}
}
We present a formulation of flow matching as variational inference, which we refer to as variational flow matching (VFM). We use this formulation to develop CatFlow, a flow matching method for categorical data that 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. VFM admits both the original flow matching objective and the CatFlow 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.
Variational Flow Matching for Graph Generation
Floor Eijkelboom Grigory Bartosh Christian A. Naesseth Max Welling Jan-Willem van de Meent