Asymptotic Analysis of Inpainting via Universal Shearlet Systems. - Martin Genzel; Gitta Kutyniok (SIAM J. Imaging Sci., 2014)

A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations. - Martin Genzel; Gitta Kutyniok (CoRR, 2016)

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. - Tim O. F. Conrad; Martin Genzel; Nada Cvetkovic; Niklas Wulkow; Alexander B. Leichtle; Jan Vybíral; Gitta Kutyniok; Christof Schütte (BMC Bioinform., 2017)

$ ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed? - Martin Genzel; Gitta Kutyniok; Maximilian März (CoRR, 2017)

High-Dimensional Estimation of Structured Signals From Non-Linear Observations With General Convex Loss Functions. - Martin Genzel (IEEE Trans. Inf. Theory, 2017)

Blind Sparse Recovery Using Imperfect Sensor Networks. - Peter Jung 001; Martin Genzel (SSP, 2018)

Robust 1-Bit Compressed Sensing via Hinge Loss Minimization. - Martin Genzel; Alexander Stollenwerk (CoRR, 2018)

Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery. - Martin Genzel; Maximilian März; Robert Seidel (CoRR, 2020)

Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20). - Martin Genzel; Maximilian März; Robert Seidel (CoRR, 2020)

A Unified Approach to Uniform Signal Recovery From Non-Linear Observations. - Martin Genzel; Alexander Stollenwerk (CoRR, 2020)

Recovering Structured Data From Superimposed Non-Linear Measurements. - Martin Genzel; Peter Jung 001 (IEEE Trans. Inf. Theory, 2020)

AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry. - Martin Genzel; Jan MacDonald; Maximilian März (CoRR, 2021)

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. - Martin Genzel; Ingo Gühring; Jan MacDonald; Maximilian März (ICML, 2022)

Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. - Theophil Trippe; Martin Genzel; Jan MacDonald; Maximilian März (CoRR, 2022)

The Separation Capacity of Random Neural Networks. - Sjoerd Dirksen; Martin Genzel; Laurent Jacques; Alexander Stollenwerk (J. Mach. Learn. Res., 2022)

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery. - Jonathan Sauder; Martin Genzel; Peter Jung 001 (IEEE J. Sel. Areas Inf. Theory, 2022)

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. - Julien Siems; Konstantin Ditschuneit; Winfried Ripken; Alma Lindborg; Maximilian Schambach; Johannes S. Otterbach; Martin Genzel (NeurIPS, 2023)

Memorization with neural nets: going beyond the worst case. - Sjoerd Dirksen; Patrick Finke; Martin Genzel (CoRR, 2023)

Self-Distilled Representation Learning for Time Series. - Felix Pieper; Konstantin Ditschuneit; Martin Genzel; Alexandra Lindt; Johannes S. Otterbach (CoRR, 2023)

Solving Inverse Problems With Deep Neural Networks - Robustness Included? - Martin Genzel; Jan MacDonald; Maximilian März (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

A Unified Approach to Uniform Signal Recovery From Nonlinear Observations. - Martin Genzel; Alexander Stollenwerk (Found. Comput. Math., 2023)

Choose Your Model Size: Any Compression by a Single Gradient Descent. - Martin Genzel; Patrick Putzky; Pengfei Zhao; Sebastian Schulze; Mattes Mollenhauer; Robert Seidel; Stefan Dietzel; Thomas Wollmann (CoRR, 2025)