Bayesian Compression for Deep Learning [article]

Christos Louizos, Karen Ullrich, Max Welling
2017 arXiv   pre-print
Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by adopting a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes instead of individual weights, and 2) we use the posterior uncertainties to determine the optimal fixed
more » ... point precision to encode the weights. Both factors significantly contribute to achieving the state of the art in terms of compression rates, while still staying competitive with methods designed to optimize for speed or energy efficiency.
arXiv:1705.08665v4 fatcat:cyutmpic25fn3a65ep4w3gombu