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BEGIN:VEVENT
DTSTAMP:20181221T160910Z
LOCATION:C140/142
DTSTART;TZID=America/Chicago:20181115T133000
DTEND;TZID=America/Chicago:20181115T150000
UID:submissions.supercomputing.org_SC18_sess190@linklings.com
SUMMARY:Deep Learning
DESCRIPTION:Paper\nApplications, Cosmology, Data Analytics, Deep Learning,
  Machine Learning, Programming Systems, Storage, Visualization, Tech Progr
 am Reg Pass\n\nCosmoFlow: Using Deep Learning to Learn the Universe at Sca
 le\n\nMathuriya, Bard, Mendygral, Meadows, Arnemann...\n\nDeep learning is
  a promising tool to determine the physical model that describes our unive
 rse.   To handle the considerable computational cost of this problem, we p
 resent CosmoFlow: a highly scalable deep learning application built on top
  of the TensorFlow framework.\n\nCosmoFlow uses efficient implem...\n\n---
 ------------------\nAnatomy of High-Performance Deep Learning Convolutions
  on SIMD Architectures\n\nGeorganas, Avancha, Banerjee, Kalamkar, Henry...
 \n\nConvolution layers are prevalent in many classes of deep neural networ
 ks, including Convolutional Neural Networks (CNNs) which provide state-of-
 the-art results for tasks like image recognition, neural machine translati
 on, and speech recognition. The computationally expensive nature of a conv
 olution ...\n\n---------------------\nExploring Flexible Communications fo
 r Streamlining DNN Ensemble Training Pipelines\n\nPittman, Guan, Shen, Lim
 , Patton\n\nParallel training of a Deep Neural Network (DNN) ensemble on a
  cluster of nodes is a common practice to train multiple models in order t
 o construct a model with a higher prediction accuracy. Existing ensemble t
 raining pipelines can perform a great deal of redundant operations, result
 ing in unnecessa...\n
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