Martin Genzel

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 27

Top Topics

Compressed Sensing

Generalized Additive Models

Signal Recovery

Feature Selection And Classification

Top Venues

IEEE Trans. Inf. Theory

IEEE J. Sel. Areas Inf. Theory

Publications

Martin Genzel, Alexander Stollenwerk
A Unified Approach to Uniform Signal Recovery From Nonlinear Observations. Found. Comput. Math. 23 (3) (2023)
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 (2023)
Martin Genzel, Jan MacDonald, Maximilian März
Solving Inverse Problems With Deep Neural Networks - Robustness Included? IEEE Trans. Pattern Anal. Mach. Intell. 45 (1) (2023)
Sjoerd Dirksen, Patrick Finke, Martin Genzel
Memorization with neural nets: going beyond the worst case. CoRR (2023)
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)
Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes S. Otterbach
Self-Distilled Representation Learning for Time Series. CoRR (2023)
Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. ICML (2022)
Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk
The Separation Capacity of Random Neural Networks. J. Mach. Learn. Res. 23 (2022)
Jonathan Sauder, Martin Genzel, Peter Jung
Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery. CoRR (2022)
Theophil Trippe, Martin Genzel, Jan MacDonald, Maximilian März
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. CoRR (2022)
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) (2022)
Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. CoRR (2022)
Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk
The Separation Capacity of Random Neural Networks. CoRR (2021)
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 (2021)
Martin Genzel, Jan MacDonald, Maximilian März
Solving Inverse Problems With Deep Neural Networks - Robustness Included? CoRR (2020)
Martin Genzel, Peter Jung
Recovering Structured Data From Superimposed Non-Linear Measurements. IEEE Trans. Inf. Theory 66 (1) (2020)
Martin Genzel, Maximilian März, Robert Seidel
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery. CoRR (2020)
Martin Genzel, Alexander Stollenwerk
A Unified Approach to Uniform Signal Recovery From Non-Linear Observations. CoRR (2020)
Martin Genzel, Maximilian März, Robert Seidel
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20). CoRR (2020)
Martin Genzel, Alexander Stollenwerk
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization. CoRR (2018)
Peter Jung, Martin Genzel
Blind Sparse Recovery Using Imperfect Sensor Networks. SSP (2018)
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) (2017)
Martin Genzel, Gitta Kutyniok, Maximilian März
$\ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed? CoRR (2017)
Martin Genzel, Peter Jung
Recovering Structured Data From Superimposed Non-Linear Measurements. CoRR (2017)
Martin Genzel
High-Dimensional Estimation of Structured Signals From Non-Linear Observations With General Convex Loss Functions. IEEE Trans. Inf. Theory 63 (3) (2017)
Martin Genzel, Gitta Kutyniok
A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations. CoRR (2016)
Martin Genzel
High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions. CoRR (2016)
Martin Genzel, Gitta Kutyniok
Asymptotic Analysis of Inpainting via Universal Shearlet Systems. SIAM J. Imaging Sci. 7 (4) (2014)