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2020 – today
- 2023
[j8]Martin Genzel, Alexander Stollenwerk:
A Unified Approach to Uniform Signal Recovery From Nonlinear Observations. Found. Comput. Math. 23(3): 899-972 (2023)- [j7]Martin Genzel
, Jan MacDonald, Maximilian März:
Solving Inverse Problems With Deep Neural Networks - Robustness Included? IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1119-1134 (2023) - [c3]Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes S. Otterbach, Martin Genzel:
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. NeurIPS 2023 - [i17]Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes S. Otterbach, Martin Genzel:
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. CoRR abs/2305.11475 (2023) - [i16]Sjoerd Dirksen, Patrick Finke, Martin Genzel:
Memorization with neural nets: going beyond the worst case. CoRR abs/2310.00327 (2023) - [i15]Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes S. Otterbach:
Self-Distilled Representation Learning for Time Series. CoRR abs/2311.11335 (2023) - 2022
- [j6]Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk:
The Separation Capacity of Random Neural Networks. J. Mach. Learn. Res. 23: 309:1-309:47 (2022) - [j5]Jonathan Sauder, Martin Genzel, Peter Jung:
Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery. IEEE J. Sel. Areas Inf. Theory 3(3): 481-492 (2022) - [c2]Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März:
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. ICML 2022: 7368-7381 - [i14]Jonathan Sauder, Martin Genzel, Peter Jung:
Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery. CoRR abs/2202.03391 (2022) - [i13]Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März:
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. CoRR abs/2206.07050 (2022) - [i12]Theophil Trippe, Martin Genzel, Jan MacDonald, Maximilian März:
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. CoRR abs/2211.10103 (2022) - 2021
- [i11]Martin Genzel, Jan MacDonald, Maximilian März:
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry. CoRR abs/2106.00280 (2021) - [i10]Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk:
The Separation Capacity of Random Neural Networks. CoRR abs/2108.00207 (2021) - 2020
- [j4]Martin Genzel, Peter Jung:
Recovering Structured Data From Superimposed Non-Linear Measurements. IEEE Trans. Inf. Theory 66(1): 453-477 (2020) - [i9]Martin Genzel, Maximilian März, Robert Seidel:
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery. CoRR abs/2001.09952 (2020) - [i8]Martin Genzel, Maximilian März, Robert Seidel:
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20). CoRR abs/2009.03694 (2020) - [i7]Martin Genzel, Alexander Stollenwerk:
A Unified Approach to Uniform Signal Recovery From Non-Linear Observations. CoRR abs/2009.09185 (2020) - [i6]Martin Genzel, Jan MacDonald, Maximilian März:
Solving Inverse Problems With Deep Neural Networks - Robustness Included? CoRR abs/2011.04268 (2020)
2010 – 2019
- 2018
- [c1]Peter Jung, Martin Genzel:
Blind Sparse Recovery Using Imperfect Sensor Networks. SSP 2018: 598-602 - [i5]Martin Genzel, Alexander Stollenwerk:
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization. CoRR abs/1804.04846 (2018) - 2017
- [j3]Tim O. F. Conrad, Martin Genzel, Nada Cvetkovic, Niklas Wulkow, Alexander B. Leichtle, Jan Vybíral, Gitta Kutyniok, Christof Schütte:
Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. BMC Bioinform. 18(1): 160:1-160:20 (2017) - [j2]Martin Genzel:
High-Dimensional Estimation of Structured Signals From Non-Linear Observations With General Convex Loss Functions. IEEE Trans. Inf. Theory 63(3): 1601-1619 (2017) - [i4]Martin Genzel, Peter Jung:
Recovering Structured Data From Superimposed Non-Linear Measurements. CoRR abs/1708.07451 (2017) - [i3]Martin Genzel, Gitta Kutyniok, Maximilian März:
$\ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed? CoRR abs/1710.04952 (2017) - 2016
- [i2]Martin Genzel:
High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions. CoRR abs/1602.03436 (2016) - [i1]Martin Genzel, Gitta Kutyniok:
A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations. CoRR abs/1608.08852 (2016) - 2014
- [j1]Martin Genzel, Gitta Kutyniok:
Asymptotic Analysis of Inpainting via Universal Shearlet Systems. SIAM J. Imaging Sci. 7(4): 2301-2339 (2014)
Coauthor Index
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