Stochastic Bayesian Neural Networks
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Bayesian neural networks perform variational inference over weights but cal- culation of the posterior distribution remains a challenge. Our work builds on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound. In this paper, we present a stochastic bayesian neural network in which we maximize Evidence Lower Bound using a new objective function which we name as Stochastic Evidence Lower Bound. We tested our approach on 5 publicly available UCI datasets using test RMSE and log likelihood as the evaluation metrics. We demonstrate that our work not only beats the previous state of the art algorithms but also allows uncertainty quantification and is scalable to larger datasets.
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