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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20181221T160726Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181111T143000
DTEND;TZID=America/Chicago:20181111T150000
UID:submissions.supercomputing.org_SC18_sess221_ws_mlhpce128@linklings.com
SUMMARY:Deep Learning Evolutionary Optimization for Regression of Rotorcra
 ft Vibrational Spectra
DESCRIPTION:Workshop\nApplications, Deep Learning, Machine Learning, Works
 hop Reg Pass\n\nDeep Learning Evolutionary Optimization for Regression of 
 Rotorcraft Vibrational Spectra\n\nMartinez-Gonzalez, Brewer\n\nA method fo
 r Deep Neural Network (DNN) hyperparameter search using evolutionary optim
 ization is proposed for nonlinear high-dimensional multivariate regression
  problems. Deep networks often lead to extensive hyperparameter searches w
 hich can become an ambiguous process due to network complexity. Therefore,
  we propose a user-friendly method that integrates Dakota optimization lib
 rary, TensorFlow, and Galaxy HPC workflow management tool to deploy massiv
 ely parallel function evaluations in a Genetic Algorithm (GA). Deep Learni
 ng Evolutionary Optimization (DLEO) is the current GA implementation being
  presented. Compared with random generated and hand-tuned models, DLEO pro
 ved to be significantly faster and better searching for optimal architectu
 re hyperparameter configurations. Implementing DLEO allowed us to find mod
 els with higher validation accuracies at lower computational costs in less
  than 72 hours, as compared with weeks of manual and random search. Moreov
 er, parallel coordinate plots provided valuable insights about network arc
 hitecture designs and their regression capabilities
URL:https://sc18.supercomputing.org/presentation/?id=ws_mlhpce128&sess=ses
 s221
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