Martin Genzel

According to our database, Martin Genzel authored at least 22 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

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, February, 2025

2023

A Unified Approach to Uniform Signal Recovery From Nonlinear Observations.

Martin Genzel, Alexander Stollenwerk

Found. Comput. Math., June, 2023

Solving Inverse Problems With Deep Neural Networks - Robustness Included?

Martin Genzel, Jan MacDonald, Maximilian März

IEEE Trans. Pattern Anal. Mach. Intell., 2023

Self-Distilled Representation Learning for Time Series.

Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes S. Otterbach

CoRR, 2023

Memorization with neural nets: going beyond the worst case.

Sjoerd Dirksen, Patrick Finke, Martin Genzel

CoRR, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery.

Jonathan Sauder, Martin Genzel, Peter Jung

IEEE J. Sel. Areas Inf. Theory, September, 2022

The Separation Capacity of Random Neural Networks.

Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk

J. Mach. Learn. Res., 2022

Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images.

Theophil Trippe, Martin Genzel, Jan MacDonald, Maximilian März

CoRR, 2022

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning.

Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März

Proceedings of the International Conference on Machine Learning, 2022

2021

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

2020

Recovering Structured Data From Superimposed Non-Linear Measurements.

Martin Genzel, Peter Jung

IEEE Trans. Inf. Theory, 2020

A Unified Approach to Uniform Signal Recovery From Non-Linear Observations.

Martin Genzel, Alexander Stollenwerk

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

Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery.

Martin Genzel, Maximilian März, Robert Seidel

CoRR, 2020

2018

Robust 1-Bit Compressed Sensing via Hinge Loss Minimization.

Martin Genzel, Alexander Stollenwerk

CoRR, 2018

Blind Sparse Recovery Using Imperfect Sensor Networks.

Peter Jung, Martin Genzel

Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, 2018

2017

High-Dimensional Estimation of Structured Signals From Non-Linear Observations With General Convex Loss Functions.

Martin Genzel

IEEE Trans. Inf. Theory, 2017

$\ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed?

Martin Genzel, Gitta Kutyniok, Maximilian März

CoRR, 2017

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

2016

A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations.

Martin Genzel, Gitta Kutyniok

CoRR, 2016

2014

Asymptotic Analysis of Inpainting via Universal Shearlet Systems.

Martin Genzel, Gitta Kutyniok

SIAM J. Imaging Sci., 2014