52 citations · 162 across the 24 of their papers we have counts for
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cs.LG2022★ 3 cited
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu, Moksh Jain, Bonaventure Dossou +10
Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However,…
cs.LG2022★ 21 cited
Bayesian Structure Learning with Generative Flow Networks
Tristan Deleu, António Góis, Chris Emezue +4
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distr…