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BEGIN:VEVENT
DTSTAMP:20260522T150153Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181111T140000
DTEND;TZID=America/Chicago:20181111T173000
UID:submissions.supercomputing.org_SC18_sess221@linklings.com
SUMMARY:Machine Learning in HPC Environments
DESCRIPTION:Deep Learning Evolutionary Optimization for Regression of Roto
 rcraft Vibrational Spectra\n\nA method for Deep Neural Network (DNN) hyper
 parameter search using evolutionary optimization is proposed for nonlinear
  high-dimensional multivariate regression problems. Deep networks often le
 ad to extensive hyperparameter searches which can become an ambiguous proc
 ess due to network complexity. The...\n\n\nDaniel A. Martinez-Gonzalez (US
  Army Engineer Research and Development Center) and Wesley Brewer (US Depa
 rtment of Defense HPC Modernization Program)\n---------------------\nAutom
 ated Parallel Data Processing Engine with Application to Large-Scale Featu
 re Extraction\n\nAs new scientific instruments generate ever more data, we
  need to parallelize advanced data analysis algorithms such as machine lea
 rning to harness the available computing power. The success of commercial 
 Big Data systems demonstrated that it is possible to automatically paralle
 lize these algorithms...\n\n\nXin Xing (Georgia Institute of Technology) a
 nd Bin Dong, Jonathan Ajo-Franklin, and Kesheng Wu (Lawrence Berkeley Nati
 onal Laboratory)\n---------------------\nIntroduction - Machine Learning i
 n HPC Environments\n\nThe intent of this workshop is to bring together res
 earchers, practitioners, and scientific communities to discuss methods tha
 t utilize extreme scale systems for machine learning. This workshop will f
 ocus on the greatest challenges in utilizing HPC for machine learning and 
 methods for exploiting dat...\n\n\nSteven R. Young and Robert M. Patton (O
 ak Ridge National Laboratory), Janis Keuper (Fraunhofer Institute for Indu
 strial Mathematics), and Michael Houston (Nvidia Corporation)\n-----------
 ----------\nAuto-Tuning TensorFlow Threading Model for CPU Backend\n\nTens
 orFlow is a popular deep learning framework used to solve machine learning
  and deep learning problems such as image classification and speech recogn
 ition. It also allows users to train neural network models or deploy them 
 for inference using GPUs, CPUs, and custom-designed hardware such as TPUs.
 ...\n\n\nNiranjan Hasabnis (Intel Corporation)\n---------------------\nTra
 ining Speech Recognition Models on HPC Infrastructure\n\nAutomatic speech 
 recognition is used extensively in speech interfaces and spoken dialogue s
 ystems. To accelerate the development of new speech recognition models and
  techniques, developers at Mozilla have open sourced a deep learning based
  Speech-To-Text engine known as project DeepSpeech based on B...\n\n\nDeep
 thi Karkada and Vikram A. Saletore (Intel Corporation)\n------------------
 ---\nRamifications of Evolving Misbehaving Convolutional Neural Network Ke
 rnel and Batch Sizes\n\nDeep-learners have many hyper-parameters including
  learning rate, batch size, kernel size --- all playing a significant role
  toward estimating high quality models.  Discovering useful hyper-paramete
 r guidelines is an active area of research, though the state of the art ge
 nerally uses a brute force, ...\n\n\nMark Coletti, Dalton Lunga, Anne Berr
 es, Jibonananda Sanyal, and Amy Rose (Oak Ridge National Laboratory)\n----
 -----------------\nWorkshop Morning Break\n\nTag: Applications, Deep Learn
 ing, Machine Learning\n\nRegistration Category: Workshop Reg Pass
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