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LOCATION:HG E 3
DTSTART;TZID=Europe/Stockholm:20190612T130000
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UID:submissions.pasc-conference.org_PASC19_sess126@linklings.com
SUMMARY:MS06 - Scalable Distributed Deep Learning
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Emerg
 ing Application Domains, Physics, Life Sciences\n\nCosmological Parameter 
 Estimation with Deep Learning\n\nPanyasantisuk, Schöngens, Herbel, Kacprza
 k, Refregier\n\nThe point spread function (PSF) describes the smearing of 
 astronomical images due to Earth's atmosphere and imperfections in the opt
 ics. When constraining cosmological models using imaging data, it is very 
 important to account for the PSF, since it can bias the results when left 
 uncorrected. Recentl...\n\n---------------------\nUsing Deep Learning to A
 ccurately Detect Cancer Mutations\n\nEbrahim Sahraeian, Liu, Lau, Podesta,
  Miroslaw...\n\nWe present NeuSomatic, the first convolutional neural netw
 ork-based approach for identifying cancer mutations. NeuSomatic’s ve
 rsatility enables its use on DNA sequence data generated from different se
 quencing platforms, sequencing strategies, and tumor purities while signif
 icantly outperformin...\n\n---------------------\nRecent Trends in Distrib
 uted Training - Sparsified, Compressed and Local SGD\n\nStich, Karimireddy
 , Cordonnier, Rebjock, Patel...\n\nWe discuss recent techniques to improve
  distributed training of machine learning models, which are also of intere
 st on HPC systems. First, SGD with sparsified or compressed gradients, suc
 h as signSGD, are key techniques offering orders-of-magnitude improvements
  in scalability and communication requi...\n\n---------------------\nDemys
 tifying Parallel and Distributed Deep Learning\n\nBen-Nun, Hoefler\n\nDeep
  Neural Networks (DNNs) are becoming an important tool in modern computing
  applications. Accelerating their training is a major challenge and techni
 ques range from distributed algorithms to low-level circuit design. The ta
 lk outlines deep learning from a theoretical perspective, followed by appr
 ...\n
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