Efficient Posterior Approximation for Bayesian Neural Networks in Large-Scale Distributed Environments
Bayesian neural networks (BNNs) introduce a principled framework for uncertainty estimation through learning probability distributions with respect to model parameters. Still, current deep networks are not only unfeasible to perform exact Bayesian inference, but traditional posterior approximation methods also incur si...