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Iain Murray

 

 

Wednesday 18th June 2014

Time: 4pm

 

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

Flexible and deep models for density estimation

 

We bring the empirical success of deep neural network models in
regression and classification to high-dimensional density estimation.
Using the product rule, density estimation in D-dimensions can be
reduced to D regression tasks. We tie these tasks together to improve
computational and statistical efficiency, obtaining state-of-the-art
fits across a wide range of benchmark tasks. I'll give an example
Bayesian data analysis application from cosmology.

Work with Benigno Uria and Hugo Larochelle.
http://homepages.inf.ed.ac.uk/imurray2/pub/13rnade/
http://arxiv.org/abs/1310.1757