[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.
