Stochastic Bayesian Neural Networks

• URL: http://arxiv.org/abs/2008.07587v3
• Date: Mon, 21 Jun 2021 18:12:29 GMT
• Title: Stochastic Bayesian Neural Networks
• Authors: Abhinav Sagar
• Abstract summary: We build on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound. We present a bayesian neural network in which we maximize Evidence Lower Bound using a new objective function which we name as Evidence Lower Bound.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian neural networks perform variational inference over the weights however calculation 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 evaluate our network 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 is also scalable to larger datasets.

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