| 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 | 147 | 2022 |
| 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 | 54 | 2016 |
| ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed? M Genzel, G Kutyniok, M März Applied and Computational Harmonic Analysis, 2020 | 43 | 2020 |
| Asymptotic analysis of inpainting via universal shearlet systems M Genzel, G Kutyniok SIAM Journal on Imaging Sciences 7 (4), 2301-2339, 2014 | 42 | 2014 |
| 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 | 37 | 2017 |
| 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 | 33 | 2022 |
| Recovering structured data from superimposed non-linear measurements M Genzel, P Jung IEEE Transactions on Information Theory 66 (1), 453-477, 2020 | 19 | 2020 |
| A mathematical framework for feature selection from real-world data with non-linear observations M Genzel, G Kutyniok arXiv preprint arXiv:1608.08852, 2016 | 14 | 2016 |
| 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 | 12 | 2022 |
| 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 | 12 | 2014 |
| A Unified Approach to Uniform Signal Recovery From Nonlinear Observations M Genzel, A Stollenwerk Foundations of Computational Mathematics 23 (3), 899-972, 2023 | 10 | 2023 |
| The Separation Capacity of Random Neural Networks S Dirksen, M Genzel, L Jacques, A Stollenwerk Journal of Machine Learning Research 23 (309), 2022 | 10 | 2022 |
| 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 | 10 | 2020 |
| Robust 1-Bit Compressed Sensing via Hinge Loss Minimization M Genzel, A Stollenwerk 2019 13th International conference on Sampling Theory and Applications (SampTA), 2020 | 10 | 2020 |
| 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 | 10 | 2019 |
| The Mismatch Principle: The Generalized Lasso Under Large Model Uncertainties M Genzel, G Kutyniok arXiv preprint arXiv:1808.06329, 2018 | 10* | 2018 |
| 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 | 9 | 2022 |
| Curve your enthusiasm: Concurvity regularization in differentiable generalized additive models J Siems, K Ditschuneit, W Ripken, A Lindborg, M Schambach, J Otterbach, ... Advances in Neural Information Processing Systems 36, 19029-19057, 2023 | 8 | 2023 |
| 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 | 6 | 2021 |
| Solving inverse problems with deep neural networks–robustness included?. arXiv preprint.(2020) M Genzel, J Macdonald, M März arXiv preprint arXiv:2011.04268, 0 | 6 | |
Maximilian MärzTechnische Universität BerlinVerified email at math.tu-berlin.de