LIR unifies EM, belief propagation, adversarial training, GANs, and GFlowNets as instances of local inconsistency resolution on Probabilistic Dependency Graphs and demonstrates a better GFlowNet loss on synthetic data.
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Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
LIR unifies EM, belief propagation, adversarial training, GANs, and GFlowNets as instances of local inconsistency resolution on Probabilistic Dependency Graphs and demonstrates a better GFlowNet loss on synthetic data.