Feb. 9, 2024, 5:42 a.m. | Lazar Atanackovic Emmanuel Bengio

cs.LG updates on arXiv.org arxiv.org

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 favourable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. In particular, …

applications cs.lg flow framework functions generative generative models networks probability space spaces training

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