Introduction to GFlowNet

REALML Online reading group
realworldml.github.io/
Abstract: GFlowNet, short for Generative Flow Network, is a new generative modeling framework which we think is particularly suited for discrete, combinatorial objects. The idea behind GFN is to estimate (graph-theoretic) flows in a directed acyclic network. The network represents all possible ways of constructing an object, and so knowing the flow gives us a policy which we can follow to sequentially construct objects in an energy-based fashion (i.e., objects are drawn proportionally to some quantity, like a reward or unnormalized density). In this talk I’ll present the framework, the intuition behind it, some nice properties it has, some cool things we can do with it and some really cool avenues for future work such as drug discovery.

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