Investigating Generalization Behaviours of Generative Flow Networks

Understand

Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spaces.

• Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training.
• This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favorable generalization properties.
• In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets.