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cs.LG2023
Learning Energy Decompositions for Partial Inference of GFlowNets
Hyosoon Jang, Minsu Kim, Sungsoo Ahn
This paper studies generative flow networks (GFlowNets) to sample objects from the Boltzmann energy distribution via a sequence of actions. In particular, we focus on improving GFl…
cs.LG2023
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim, Joohwan Ko, Taeyoung Yun +6
GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce…
cs.LG2023
Local Search GFlowNets
Minsu Kim, Taeyoung Yun, Emmanuel Bengio +4
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ab…