default search action
Martin Genzel
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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
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
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
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
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
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
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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-09-13 01:41 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: