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GTC ON-DEMAND

HPC and Supercomputing
Presentation
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Adapting Deep Learning to New Data Using ORNL's Titan Supercomputer
Abstract:
There has been a surge of success in using deep learning as it has provided a new state of the art for a variety of domains. While these models learn their parameters through data-driven methods, model selection through hyper-parameter choices remains a tedious and highly intuition-driven task. We've developed two approaches to address this problem. Multi-node evolutionary neural networks for deep learning (MENNDL) is an evolutionary approach to performing this search. MENNDL is capable of evolving not only the numeric hyper-parameters, but is also capable of evolving the arrangement of layers within the network. The second approach is implemented using Apache Spark at scale on Titan. The technique we present is an improvement over hyper-parameter sweeps because we don't require assumptions about independence of parameters and is more computationally feasible than grid-search.
 
Topics:
HPC and Supercomputing, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7200
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