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Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning


Jun 14, 2022
Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

* ICML 2022 (long talk). Code available at https://github.com/jmaces/aapm-ct-challenge. arXiv admin note: text overlap with arXiv:2106.00280 

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


Feb 07, 2022
Jonathan Sauder, Martin Genzel, Peter Jung


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The Separation Capacity of Random Neural Networks


Jul 31, 2021
Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk


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AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry


Jun 01, 2021
Martin Genzel, Jan Macdonald, Maximilian März

* This is a technical report of a method participating in a not yet finished challenge. Therefore, it does not contain any final results. In particular, the reported reconstruction errors are only with respect to our own validation split of the provided training data. Once the official challenge report is released, these values will be updated with the results from the actual test set 

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Solving Inverse Problems With Deep Neural Networks -- Robustness Included?


Nov 09, 2020
Martin Genzel, Jan Macdonald, Maximilian März


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Generic Error Bounds for the Generalized Lasso with Sub-Exponential Data


May 18, 2020
Martin Genzel, Christian Kipp


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The Mismatch Principle: Statistical Learning Under Large Model Uncertainties


Aug 20, 2018
Martin Genzel, Gitta Kutyniok


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Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data


Nov 26, 2016
Tim Conrad, Martin Genzel, Nada Cvetkovic, Niklas Wulkow, Alexander Leichtle, Jan Vybiral, Gitta Kutyniok, Christof Schütte

* BMC Bioinform. 18 (2017), 160 

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


Aug 31, 2016
Martin Genzel, Gitta Kutyniok


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