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Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

McGill, Mason and Perona, Pietro (2017) Deciding How to Decide: Dynamic Routing in Artificial Neural Networks. Proceedings of Machine Learning Research, 70 . pp. 2363-2372. ISSN 1938-7228. https://resolver.caltech.edu/CaltechAUTHORS:20180614-120024280

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Abstract

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images. Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://proceedings.mlr.press/v70/mcgill17a.htmlPublisherArticle
http://arxiv.org/abs/1703.06217arXivArticle
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2017 by the author(s). This work was funded by a generous grant from Google Inc. We would also like to thank Krzysztof Chalupka, Cristina Segalin, and Oisin Mac Aodha for their thoughtful comments.
Funders:
Funding AgencyGrant Number
GoogleUNSPECIFIED
Record Number:CaltechAUTHORS:20180614-120024280
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180614-120024280
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87106
Collection:CaltechAUTHORS
Deposited By: Caroline Murphy
Deposited On:14 Jun 2018 20:53
Last Modified:03 Oct 2019 19:52

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