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Bayesian Compression for Deep Learning
[article]
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
arXiv:1705.08665v4
fatcat:cyutmpic25fn3a65ep4w3gombu