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BEGIN:VEVENT
DTSTAMP:20181221T160911Z
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:Workshop\nApplications, Deep Learning, Machine Learning, Works
 hop Reg Pass\n\nAuto-Tuning TensorFlow Threading Model for CPU Backend\n\n
 Hasabnis\n\nTensorFlow is a popular deep learning framework used to solve 
 machine learning and deep learning problems such as image classification a
 nd speech recognition. It also allows users to train neural network models
  or deploy them for inference using GPUs, CPUs, and custom-designed hardwa
 re such as TPUs....\n\n---------------------\nIntroduction - Machine Learn
 ing in HPC Environments\n\nYoung, Patton, Keuper, Houston\n\nThe intent of
  this workshop is to bring together researchers, practitioners, and scient
 ific 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 dat...\n\n--
 -------------------\nRamifications of Evolving Misbehaving Convolutional N
 eural Network Kernel and Batch Sizes\n\nColetti, Lunga, Berres, Sanyal, Ro
 se\n\nDeep-learners have many hyper-parameters including learning rate, ba
 tch size, kernel size --- all playing a significant role toward estimating
  high quality models.  Discovering useful hyper-parameter guidelines is an
  active area of research, though the state of the art generally uses a bru
 te force, ...\n\n---------------------\nAutomated Parallel Data Processing
  Engine with Application to Large-Scale Feature Extraction\n\nXing, Dong, 
 Ajo-Franklin, Wu\n\nAs new scientific instruments generate ever more data,
  we need to parallelize advanced data analysis algorithms such as machine 
 learning to harness the available computing power. The success of commerci
 al Big Data systems demonstrated that it is possible to automatically para
 llelize these algorithms...\n\n---------------------\nDeep Learning Evolut
 ionary Optimization for Regression of Rotorcraft Vibrational Spectra\n\nMa
 rtinez-Gonzalez, Brewer\n\nA method for Deep Neural Network (DNN) hyperpar
 ameter search using evolutionary optimization is proposed for nonlinear hi
 gh-dimensional multivariate regression problems. Deep networks often lead 
 to extensive hyperparameter searches which can become an ambiguous process
  due to network complexity. The...\n\n---------------------\nTraining Spee
 ch Recognition Models on HPC Infrastructure\n\nKarkada, Saletore\n\nAutoma
 tic speech recognition is used extensively in speech interfaces and spoken
  dialogue systems. To accelerate the development of new speech recognition
  models and techniques, developers at Mozilla have open sourced a deep lea
 rning based Speech-To-Text engine known as project DeepSpeech based on B..
 .\n\n---------------------\nWorkshop Morning Break\n\n\n
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