Inhalt des Dokuments
Publikationen
Nr. | Titel |
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1 | P. Grohs, G. Kutyniok, J. Ma, P. Petersen Anisotropic multiscale systems on bounded domains, 2015 Download |
2 | H. Bölcskei, P. Grohs, G. Kutyniok, P. Petersen Optimal Approximation with Sparsely Connected Deep Neural Networks, 2017, arxiv.org/abs/1705.01714Download |
3 | P. Petersen, F. Voigtlaender, Optimal approximation of piecewise smooth functions using deep ReLU neural networks, 2017, Download |
4 | P. Petersen, M. Raslan, Approximation properties of shearlet frames for Sobolev Spaces, 2017, Download |
Nr. | Titel |
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1 | J. Ma, P. Petersen Linear independence of compactly supported separable shearlet systems, J. Math. Anal. Appl. Volume 428, Issue 1, Pages 238–257, 2015. Download |
2 | G. Kutyniok, P. Petersen Classification of Edges Using Compactly Supported Shearlets, Appl. Comput. Harmon. Anal., 42(2), 245--293,2017. Download |
3 | P. Petersen Shearlet approximation of functions with discontinuous derivatives, J. Approx. Theory., 207, 127--138, 2016. Download |
4 | G. Kutyniok, V. Mehrmann, and P. Petersen Regularization and Numerical Solution of the Inverse Scattering Problem using Shearlet Frames, J. Inverse Ill-Posed Probl., 25(3), 287--309, 2017. Download |
5 | Ch. Lessig, P. Petersen, M. Schäfer Bendlets: A Second-Order Shearlet Transform with Bent Elements, Appl. Comput. Harmon. Anal. in Press. Download |
Nr. | Titel |
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1 | H. Bölcskei, P. Grohs, G. Kutyniok, P. Petersen, Memory-optimal neural network approximation, Proc. of SPIE (Wavelets and Sparsity XVII), San Diego, USA, 2017, to appear |
Nr. | Titel |
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1 | Shearlets on Bounded Domains and Analysis of Singularities Using Compactly Supported Shearlets, Dissertation, Technische Universität Berlin, 2016. |
2 | Applications of Shearlet Frames for a Sparsity Promoting Regularization of the Inverse Scattering Problem, Masterarbeit, Technische Universität Berlin, 2013. |
3 | Nonnegative completions of block operators, Bachelorarbeit, Technische Universität Berlin, 2011. |
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1 | Lecture Notes for the workshop: Approximation with Deep Neural Networks at the SFB Workshop 2017 "DGD Days". |
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