GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

HPC and Supercomputing
Presentation
Media
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
Type:
Talk
Event:
SIGGRAPH
Year:
2017
Session ID:
SC1704
Download:
Share: