Geomstats: A Python Package for Riemannian Geometry in Machine Learning

Autor: Miolane, Nina, Brigant, Alice Le, Mathe, Johan, Hou, Benjamin, Guigui, Nicolas, Thanwerdas, Yann, Heyder, Stefan, Peltre, Olivier, Koep, Niklas, Zaatiti, Hadi, Hajri, Hatem, Cabanes, Yann, Gerald, Thomas, Chauchat, Paul, Shewmake, Christian, Kainz, Bernhard, Donnat, Claire, Holmes, Susan, Pennec, Xavier

We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-o

Externí odkaz: http://arxiv.org/abs/2004.04667

The Restricted Isometry Property of Block Diagonal Matrices for Group-Sparse Signal Recovery

Autor: Koep, Niklas, Behboodi, Arash, Mathar, Rudolf

Group-sparsity is a common low-complexity signal model with widespread application across various domains of science and engineering. The recovery of such signal ensembles from compressive measurements has been extensively studied in the literature u

Externí odkaz: http://arxiv.org/abs/1901.06214

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Autor: Townsend, James, Koep, Niklas, Weichwald, Sebastian

Publikováno v: Journal of Machine Learning Research, 17(137):1-5, 2016 ( https://jmlr.org/papers/v17/16-177.html )

Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold. W

Externí odkaz: http://arxiv.org/abs/1603.03236

Quantized compressive sampling for structured signal estimation

Autor: Koep, Niklas

Publikováno v: Aachen : Mainz 1 Online-Ressource (x, 189 Seiten) : Illustrationen (2019). doi:10.18154/RWTH-2019-06704 = Dissertation, RWTH Aachen University, 2019

This thesis investigates different approaches to enable the use of compressed sensing (CS)-based acquisition devices in resource-constrained environments relying on cheap, energy-efficient sensors. We consider the acquisition of structured low-comple

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______791::a8bbd2ce666ed0e27e50c4c17321bd12
https://publications.rwth-aachen.de/record/764234

Noise-shaping for closed-loop Multi-Channel Linear Prediction.

Autor: Koep, Niklas, Schafer, Magnus, Vary, Peter

Publikováno v: 2015 IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP); 2015, p394-398, 5p