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Theoretical ML

2024

Analyzing GFlowNets: Stability, Expressiveness, and Assessment

·288 words
Tiago Da Silva Eliezer De Souza Da Silva Rodrigo Barreto Alves Luiz Max Carvalho Amauri H Souza Samuel Kaski Vikas Garg Diego Mesquita Publication Preprint Workshop SPIGM@ICML Deep Generative Models Theoretical ML Generative Flow Networks

How balance violations impact the learned distribution, motivating an weighted balance loss to improve training. For graph distributions, there are scenarios where balance is unattainable, and richer embeddings of children’s states is needed enhance expressiveness. To measure of distributional correctness in GFN we introduce a provable correct novel assessment metric.