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[00960] Hierarchical Low Rank Tensors and DNNs for High-dimensional Approximation

  • Session Date & Time :
    • 00960 (1/3) : 3C (Aug.23, 13:20-15:00)
    • 00960 (2/3) : 3D (Aug.23, 15:30-17:10)
    • 00960 (3/3) : 3E (Aug.23, 17:40-19:20)
  • Type : Proposal of Minisymposium
  • Abstract : The minisymposium aims at bridging the gap between low rank tensors and neural networks for learning of high-dimensional functions, in particular in the context of uncertainty quantification. The talks will highlight different aspects ranging from approximation to optimization. The underlying motivation is to understand strengths and difficulties of network based representations and to identify structures and techniques that can be combined beneficially.
  • Organizer(s) : Martin Eigel, Lars Grasedyck
  • Classification : 65C40, 65N55, 65N30, 68T07
  • Speakers Info :
    • Maren Klever (RWTH Aachen)
    • Nando Farchmin (Physikalisch-Technische Bundesanstalt Berlin)
    • Sebastian Kraemer (RWTH Aachen)
    • Thong Le (RWTH Aachen)
    • Dima Moser (RWTH Aachen)
    • Philipp Trunschke (Nantes Université)
    • Andreas Zeiser (HTW Berlin)
    • Tim Werthmann (RWTH Aachen)
    • Janina Enrica Schütte (WIAS Berlin)
    • Robert Gruhlke (WIAS Berlin)
    • Mathias Oster (TU Berlin)
  • Talks in Minisymposium :