Abstract
Deep convolutional neural networks (CNNs) have become extremely popular and successful at a number of machine learning tasks. One of the great challenges of successfully deploying a CNN is designing the network: specifying the network topology (sequence of layer types) and configuring the network (setting all the internal layer hyper-parameters). There are a number of techniques which are commonly used to design the network. One of the most successful is a simple (but lengthy) random search. In this paper we demonstrate how a random search can be dramatically improved by a two-phase search. The first phase is a traditional random search on n network configurations. The second phase exploits a support vector machine to guide a second random search on N network configurations. We apply this technique to a dataset containing satellite imagery and demonstrate that we can, with very high accuracy, identify regions containing clouds which obscure the landscape below.
Original language | English |
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Title of host publication | Proceedings of MLHPC 2017 |
Subtitle of host publication | Machine Learning in HPC Environments - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis |
Publisher | Association for Computing Machinery, Inc |
ISBN (Electronic) | 9781450351379 |
DOIs | |
State | Published - Nov 12 2017 |
Event | 2017 Machine Learning in HPC Environments, MLHPC 2017 - Denver, United States Duration: Nov 12 2017 → Nov 17 2017 |
Publication series
Name | Proceedings of MLHPC 2017: Machine Learning in HPC Environments - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
Conference | 2017 Machine Learning in HPC Environments, MLHPC 2017 |
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Country/Territory | United States |
City | Denver |
Period | 11/12/17 → 11/17/17 |
Funding
This research used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the Department of Energy under Contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. MLHPC’17, November 12–17, 2017, Denver, CO, USA © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery. ACM ISBN 978-1-4503-5137-9/17/11...$15.00 https://doi.org/10.1145/3146347.3146352
Keywords
- Deep learning
- Hyper-parameter optimization
- Image quality