Reviews: Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections

This paper proposes BRAINet as to combine Bayesian structure learning and Bayesian neural networks. In detail, the method assumes a confounder on the input features X and the discriminative network parameters \phi, where this confounder is defined as the generative graph structure on X, and the discriminative network shares the same structure as the generative one. Given observations X and Y, the approach first sample the generative graph structure from the posterior given X, then train the parameters of the corresponding discriminative network in order to fit the posterior distribution of phi given X and Y. Experiments are performed on calibration and OOD tasks, with MC-dropout and deep Ensembles as the main comparing baselines. Reviewers include experts in Bayesian structure learning and Bayesian neural networks. They read the author feedback carefully and engaged in post-rebuttal discussion actively.