Niklas Koep

13 papers A 1B 1Misc 2Journal 6Unranked 3
YearRankTypeTitle / Venue / Authors
2020 A conf
Adversarial Risk Bounds through Sparsity based Compression.
AISTATS
Emilio Rafael Balda, Niklas Koep, Arash Behboodi, Rudolf Mathar
2020 J jnl
Geomstats: A Python Package for Riemannian Geometry in Machine Learning.
CoRR
Nina Miolane, Alice Le Brigant, Johan Mathe, Benjamin Hou, Nicolas Guigui, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Bernhard Kainz, Claire Donnat, Susan P. Holmes, Xavier Pennec
2020 — conf
Introduction to Geometric Learning in Python with Geomstats.
SciPy
Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Daniel Brooks, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Bernhard Kainz, Claire Donnat, Susan P. Holmes, Xavier Pennec
2019 J jnl
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression.
CoRR
Emilio Rafael Balda, Arash Behboodi, Niklas Koep, Rudolf Mathar
2019 J jnl
Efficient Implementation of Density Evolution for Punctured Polar Codes.
IEEE Access
Christopher Schnelling, Markus Rothe, Niklas Koep, Rudolf Mathar, Anke Schmeink
2019 Misc conf
Performance Analysis of One-bit Group-sparse Signal Reconstruction.
ICASSP
Niklas Koep, Arash Behboodi, Rudolf Mathar
2019 B conf
The Group Restricted Isometry Property for Subgaussian Block Diagonal Matrices.
ISIT
Niklas Koep, Arash Behboodi, Rudolf Mathar
2019 J jnl
The Restricted Isometry Property of Block Diagonal Matrices for Group-Sparse Signal Recovery.
CoRR
Niklas Koep, Arash Behboodi, Rudolf Mathar
2018 — conf
Block-Sparse Signal Recovery From Binary Measurements.
SSP
Niklas Koep, Rudolf Mathar
2017 — conf
Binary Iterative Hard Thresholding for Frequency-Sparse Signal Recovery.
WSA
Niklas Koep, Rudolf Mathar
2016 J jnl
Pymanopt: A Python Toolbox for Manifold Optimization using Automatic Differentiation.
CoRR
James Townsend, Niklas Koep, Sebastian Weichwald
2016 J jnl
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation.
J. Mach. Learn. Res.
James Townsend, Niklas Koep, Sebastian Weichwald
2015 Misc conf
Noise-shaping for closed-loop Multi-Channel Linear Prediction.
ICASSP
Niklas Koep, Magnus Schaefer, Peter Vary