Direct Bethe free energy minimization for BNNs produces losses strictly between MAP and ELBO, enables joint empirical Bayes in one gradient pass, and matches reference methods on UCI benchmarks at single-pass cost.
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Direct Bethe Free Energy Minimization for Bayesian Neural Network
Direct Bethe free energy minimization for BNNs produces losses strictly between MAP and ELBO, enables joint empirical Bayes in one gradient pass, and matches reference methods on UCI benchmarks at single-pass cost.