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2017 GTC San Jose

S7435 - Adapting DL to New Data: An Evolutionary Algorithm for Optimizing Deep Networks

Session Speakers
Session Description

There has been a surge of success in using deep learning in imaging and speech applications for its relatively automatic feature generation and, in particular, for convolutional neural networks, high-accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, multi-node evolutionary neural networks for deep learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms. MENNDL is capable of evolving not only the numeric hyper-parameters (for example, number of hidden nodes or convolutional kernel size), but is also capable of evolving the arrangement of layers within the network.


Additional Session Information
Intermediate
Talk
Deep Learning and AI HPC and Supercomputing
Government / National Labs Higher Education / Research
25 minutes
Session Schedule