Prof. A. Nouy (École Centrale de Nantes, Frankreich)
Wednesday, January 17, 2018 - 10:00
Mohrenstr. 39, 10117 Berlin, Erhard-Schmidt-Hörsaal, Erdgeschoss
Forschungsseminar Mathematische Statistik
Tensor methods are among the most prominent tools for the approximation of high- dimensional functions. Such approximation problems naturally arise in statistical learning, stochastic analysis and uncertainty quanti cation. In many practical situations, the approximation of high- dimensional functions is made computationally tractable by using rank-structured approximations. In this talk, we give an introduction to tree-based (hierarchical) tensor formats, which can be interpreted as deep neural networks with particular architectures. Then we present adaptive algorithms for the approximation in these formats using statistical methods.
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