Events

CILVR Seminar: The Flow Matching Recipe for Generative Modeling: From Continuous to Discrete

Speaker: Ricky Chen

Location: 60 Fifth Avenue, Room 7th floor open space
Videoconference link: https://nyu.zoom.us/s/94948556744

Date: Wednesday, March 12, 2025

Flow Matching [1,2] provides a powerful and flexible framework for generative modeling by learning continuous flows that transport between probability distributions. In this talk, we introduce the fundamentals of Flow Matching, outline a very simple core principle that leads to a generalized recipe, and explore its generalizations to other stochastic processes [3]. In particular, a key focus is Discrete Flow Matching which enables principled modeling of discrete data using Continuous-Time Markov Chains [4]. Within this model class, we highlight the flexibility of using general discrete probability paths, offering new perspectives on scalable non-autoregressive discrete generative modeling [5]. [1] “Flow Matching Guide and Code” Lipman et al. 2024
[2] “Flow Matching for Generative Modeling” Lipman et al. 2022
[3] “Generator Matching: Generative modeling with arbitrary Markov processes” Holderrieth et al. 2024
[4] “Discrete Flow Matching” Gat et al. 2024
[5] “Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective” Shaul et al. 2024