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Martin Genzel
Martin Genzel
Staff Machine Learning Reseacher, Merantix Momentum
Verified email at merantix-momentum.com - Homepage
Title
Cited by
Cited by
Year
Solving Inverse Problems With Deep Neural Networks--Robustness Included?
M Genzel, J Macdonald, M März
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), 1119-1134, 2022
1102022
High-dimensional estimation of structured signals from non-linear observations with general convex loss functions
M Genzel
IEEE Transactions on Information Theory 63 (3), 1601-1619, 2016
522016
Asymptotic analysis of inpainting via universal shearlet systems
M Genzel, G Kutyniok
SIAM Journal on Imaging Sciences 7 (4), 2301-2339, 2014
402014
ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed?
M Genzel, G Kutyniok, M März
Applied and Computational Harmonic Analysis, 2020
372020
Sparse Proteomics Analysis–a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
TOF Conrad, M Genzel, N Cvetkovic, N Wulkow, A Leichtle, J Vybiral, ...
BMC bioinformatics 18, 1-20, 2017
362017
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
M Genzel, I Gühring, J Macdonald, M März
International Conference on Machine Learning, 7368-7381, 2022
222022
Recovering structured data from superimposed non-linear measurements
M Genzel, P Jung
IEEE Transactions on Information Theory 66 (1), 453-477, 2020
192020
A mathematical framework for feature selection from real-world data with non-linear observations
M Genzel, G Kutyniok
arXiv preprint arXiv:1608.08852, 2016
142016
Rietveld-based energy-dispersive residual stress evaluation: analysis of complex stress fields σij (z)
D Apel, M Klaus, M Genzel, C Genzel
Journal of Applied Crystallography 47 (2), 511-526, 2014
122014
Generic error bounds for the generalized lasso with sub-exponential data
M Genzel, C Kipp
Sampling Theory, Signal Processing, and Data Analysis 20 (2), 1-55, 2022
102022
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization
M Genzel, A Stollenwerk
2019 13th International conference on Sampling Theory and Applications (SampTA), 2020
102020
Robust 1-bit compressed sensing via hinge loss minimization
M Genzel, A Stollenwerk
Information and Inference: A Journal of the IMA 9 (2), 361-422, 2019
102019
The Mismatch Principle: The Generalized Lasso Under Large Model Uncertainties
M Genzel, G Kutyniok
arXiv preprint arXiv:1808.06329, 2018
10*2018
EDDIDAT: a graphical user interface for the analysis of energy-dispersive diffraction data
D Apel, M Genzel, M Meixner, M Boin, M Klaus, C Genzel
Journal of Applied Crystallography 53 (4), 1130-1137, 2020
92020
The Separation Capacity of Random Neural Networks
S Dirksen, M Genzel, L Jacques, A Stollenwerk
Journal of Machine Learning Research 23 (309), 2022
82022
A Unified Approach to Uniform Signal Recovery From Nonlinear Observations
M Genzel, A Stollenwerk
Foundations of Computational Mathematics 23 (3), 899-972, 2023
72023
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery
M Genzel, M März, R Seidel
Information and Inference: A Journal of the IMA 11 (1), 203-250, 2022
62022
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry
M Genzel, J Macdonald, M März
arXiv preprint arXiv:2106.00280, 2021
52021
Blind sparse recovery from superimposed non-linear sensor measurements
M Genzel, P Jung
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
52017
Artificial Neural Networks
M Genzel, G Kutyniok
GAMM Rundbrief 2, 12-18, 2019
42019
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