sponsored byACMIEEE The International Conference for High Performance 
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SCHEDULE: NOV 15-20, 2015

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MLHPC2015: Machine Learning in HPC Environments

SESSION: MLHPC2015: Machine Learning in HPC Environments

EVENT TYPE: Workshops

EVENT TAG(S): Programming Systems, SIGHPC Workshop

TIME: 2:00PM - 5:30PM

Organizer(s):Robert M. Patton, Thomas E. Potok, Barry Y. Chen, Lawrence Carin

ROOM:Hilton Salon D


The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning. This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning. Topics will include but are not limited to: machine learning models, including deep learning, for extreme scale systems; feature engineering; learning large models/optimizing hyper parameters (e.g. deep learning, representation learning); facilitating very large ensembles; applications of machine learning utilizing HPC; and future research challenges for machine learning at large scale.

This workshop will produce a refereed proceedings that will be available through the ACM Digital Library and IEEE Xplore (free of charge during and immediately after SC, and free after that to SIGHPC members).

More at: http://ornlcda.github.io/MLHPC2015/

Chair/Organizer Details:

Robert M. Patton - Oak Ridge National Laboratory

Thomas E. Potok - Oak Ridge National Laboratory

Barry Y. Chen - Lawrence Livermore National Laboratory

Lawrence Carin - Duke University

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