Poster
Bayesian Compression for Deep Learning
Christos Louizos · Karen Ullrich · Max Welling

Mon Dec 4th 06:30 -- 10:30 PM @ Pacific Ballroom #137 #None

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 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.

Author Information

Christos Louizos (University of Amsterdam)
Karen Ullrich (University of Amsterdam)

I am a Ph.D. student at Uo Amsterdam, supervised by Prof. Max Welling and alumna of the Austrian Research Institute for AI, Intelligent Music Processing and Machine Learning Group lead by Prof. Gerhard Widmer. I studied Physics and Numerical Simulations in Leipzig and Amsterdam. [CV] My research focus lies in machine learning. In particular, I am interested in statistical inference, information theory, deep learning, Bayesian methods, geometric methods and graph theory. I apply techniques of the aforementioned to problems in structural and systems biology, compression, sequential data (e.g. music, environmental data) and real-time sensoring.

Max Welling (University of Amsterdam and University of California Irvine and CIFAR)

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