Radial and Directional Posteriors for Bayesian Neural Networks [article]

Changyong Oh, Kamil Adamczewski, Mijung Park
<span title="2019-03-13">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of each neuron in terms of its magnitude; while the directional component captures the statistical dependencies among the weight parameters. The dependencies learned via the directional density provide better modeling performance compared to the widely-used Gaussian mean-field-type variational family. In addition, the
more &raquo; ... gth of input and output neurons learned via the radial density provides a structured way to compress neural networks. Indeed, experiments show that our variational family improves predictive performance and yields compressed networks simultaneously.
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