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DTSTART;TZID=Europe/Stockholm:20190612T130000
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UID:submissions.pasc-conference.org_PASC19_sess138@linklings.com
SUMMARY:MS05 - Parallel High-Dimensional Approximation: Uncertainty Quanti
 fication and Machine Learning, Part I of II
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Chemi
 stry and Materials, Physics, Engineering\n\nSparse HPC Opportunities in De
 ep Neural Networks\n\nKoanantakool\n\nWith the wide adoption of deep learn
 ing, there have been numerous efforts to accelerate deep neural networks, 
 one of which is to exploit sparsity. Many high-dimensional problems are sp
 arse by nature, and researchers have come up with abundant ways to enforce
  sparse connections. In this talk, we give...\n\n---------------------\nLe
 arning Large-Scale Sparse Graphical Models: Theory, Algorithm, and Applica
 tions\n\nSojoudi\n\nLearning models from data has a significant impact on 
 many disciplines, including computer vision, medical imaging, social netwo
 rks and signal processing. In the network inference problem, one may model
  the relationships between the network components through an underlying in
 verse covariance matrix....\n\n---------------------\nScalable Multi-Fidel
 ity Machine Learning\n\nZaspel\n\nThe solution of parametric partial diffe
 rential equations or other parametric problems is the main component of ma
 ny applications in scientific computing. To avoid the re-implementation of
  scientific simulation codes, the use of snapshot-based (non-intrusive) te
 chniques for the solution of parametri...\n\n---------------------\nHeAT -
  A New Versatile Distributed-Parallel Machine Learning Toolkit\n\nKnechtge
 s\n\nIn this talk we will present the Helmholtz Analytics Toolkit (HeAT), 
 a scientific big data analytics library for HPC systems. Big data analytic
 s, and machine learning in particular, has gained a lot of traction recent
 ly with applications ranging from neuroscience to aeronautics. At the hear
 t of many...\n
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