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Inhalt des Dokuments

Publikationen

Eingereichte Artikel
Nr.
Titel
1
P. Grohs, G. Kutyniok, J. Ma, P. Petersen
Anisotropic multiscale systems on bounded domains2015 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
Begutachtete Artikel
Nr.
Titel
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.
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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.
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5
Ch. Lessig, P. Petersen, M. Schäfer
Bendlets: A Second-Order Shearlet Transform with Bent Elements, Appl. Comput. Harmon. Anal. in Press. Download
Konferenzbeiträge
Nr.
Titel
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
Monographien
Nr.
Titel
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.

Verschiedenes
Nr.
Titel
1
Lecture Notes for the workshopApproximation with Deep Neural Networks at the SFB Workshop 2017 "DGD Days".

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