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UID:submissions.pasc-conference.org_PASC23_sess183@linklings.com
SUMMARY:AP2D - ACM Papers Session 2D
DESCRIPTION:Paper\n\nStreaming Generalized Canonical Polyadic Tensor Decom
 positions\n\nIn this paper, we develop a method which we call OnlineGCP fo
 r computing the Generalized Canonical Polyadic (GCP) tensor decomposition 
 of streaming data. GCP differs from traditional canonical polyadic (CP) te
 nsor decompositions as it allows for arbitrary objective functions which t
 he CP model attem...\n\n\nEric Phipps (Sandia National Laboratories), Nich
 olas Johnson (Cerebras Systems Inc), and Tamara Kolda (MathSci.ai)\n------
 ---------------\nUnderstanding the Computing and Analysis Needs for Resili
 ency of Power Systems from Severe Weather Impacts\n\nAs the frequency and 
 intensity of severe weather has increased, its effect on the electric grid
  has manifested in the form of significantly more and larger outages in th
 e United States. This has become especially true for regions that were pre
 viously isolated from weather extremes. In this paper, we...\n\n\nJibonana
 nda Sanyal (National Renewable Energy Laboratory); Melissa Dumas, Sangkeun
  Lee, and Supriya Chinthavali (Oak Ridge National Laboratory); Jennifer Ki
 ng (National Renewable Energy Laboratory); and Srijib Mukherjee (Oak Ridge
  National Laboratory)\n---------------------\nMixed-Precision Random Proje
 ction for RandNLA on Tensor Cores\n\nRandom projection can reduce the dime
 nsion of data while capturing its structure and is a fundamental tool for 
 machine learning, signal processing, and information retrieval, which deal
  with a large amount of data today. RandNLA (Randomized Numerical Linear A
 lgebra) leverages random projection to re...\n\n\nHiroyuki Ootomo and Rio 
 Yokota (Tokyo Institute of Technology)\n\nDomain: Engineering, Computer Sc
 ience, Machine Learning, and Applied Mathematics 
\n\nSession Chair: Johan
 nes Gebert (High-Performance Computing Center Stuttgart, University of Stu
 ttgart)
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