Repo for a paper about constructing priors on very deep models.
TeX HTML Matlab PostScript
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
figures
latex-updated
latex
source
.gitignore
LICENSE
README.md

README.md

Avoiding Pathologies in Very Deep Networks

Experiment source code and latex source for http://arxiv.org/pdf/1402.5836.pdf

Abstract:

Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

This paper appeared in the 2014 Artificial Intelligence and Statistics conference, held in Reykjavik, Iceland.

Authors: David Duvenaud, Oren Rippel, Ryan P. Adams, and Zoubin Ghahramani

Feel free to email me with any questions at (dduvenaud@seas.harvard.edu).