Martin Genzel - RankMe

32 papers A* 2Journal 29Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Float8@2bits: Entropy Coding Enables Data-Free Model Compression. CoRR Patrick Putzky, Martin Genzel, Mattes Mollenhauer, Sebastian Schulze, Thomas Wollmann, Stefan Dietzel
2025	J	`jnl`	Choose Your Model Size: Any Compression by a Single Gradient Descent. CoRR Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann
2025	J	`jnl`	Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation. Trans. Mach. Learn. Res. Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann
2024	J	`jnl`	Memorization With Neural Nets: Going Beyond the Worst Case. J. Mach. Learn. Res. Sjoerd Dirksen, Patrick Finke, Martin Genzel
2023	J	`jnl`	A Unified Approach to Uniform Signal Recovery From Nonlinear Observations. Found. Comput. Math. Martin Genzel, Alexander Stollenwerk
2023	A*	`conf`	Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. NeurIPS Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes S. Otterbach, Martin Genzel
2023	J	`jnl`	Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. CoRR Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes S. Otterbach, Martin Genzel
2023	J	`jnl`	Memorization with neural nets: going beyond the worst case. CoRR Sjoerd Dirksen, Patrick Finke, Martin Genzel
2023	J	`jnl`	Self-Distilled Representation Learning for Time Series. CoRR Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes S. Otterbach
2023	J	`jnl`	Solving Inverse Problems With Deep Neural Networks - Robustness Included? IEEE Trans. Pattern Anal. Mach. Intell. Martin Genzel, Jan MacDonald, Maximilian März
2022	J	`jnl`	Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery. CoRR Jonathan Sauder, Martin Genzel, Peter Jung
2022	J	`jnl`	Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery. IEEE J. Sel. Areas Inf. Theory Jonathan Sauder, Martin Genzel, Peter Jung
2022	J	`jnl`	Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. CoRR Theophil Trippe, Martin Genzel, Jan MacDonald, Maximilian März
2022	A*	`conf`	Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. ICML Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März
2022	J	`jnl`	Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. CoRR Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März
2022	J	`jnl`	The Separation Capacity of Random Neural Networks. J. Mach. Learn. Res. Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk
2021	J	`jnl`	AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry. CoRR Martin Genzel, Jan MacDonald, Maximilian März
2021	J	`jnl`	The Separation Capacity of Random Neural Networks. CoRR Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk
2020	J	`jnl`	A Unified Approach to Uniform Signal Recovery From Non-Linear Observations. CoRR Martin Genzel, Alexander Stollenwerk
2020	J	`jnl`	Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20). CoRR Martin Genzel, Maximilian März, Robert Seidel
2020	J	`jnl`	Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery. CoRR Martin Genzel, Maximilian März, Robert Seidel
2020	J	`jnl`	Recovering Structured Data From Superimposed Non-Linear Measurements. IEEE Trans. Inf. Theory Martin Genzel, Peter Jung
2020	J	`jnl`	Solving Inverse Problems With Deep Neural Networks - Robustness Included? CoRR Martin Genzel, Jan MacDonald, Maximilian März
2018	—	`conf`	Blind Sparse Recovery Using Imperfect Sensor Networks. SSP Peter Jung, Martin Genzel
2018	J	`jnl`	Robust 1-Bit Compressed Sensing via Hinge Loss Minimization. CoRR Martin Genzel, Alexander Stollenwerk
2017	J	`jnl`	$\ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed? CoRR Martin Genzel, Gitta Kutyniok, Maximilian März
2017	J	`jnl`	High-Dimensional Estimation of Structured Signals From Non-Linear Observations With General Convex Loss Functions. IEEE Trans. Inf. Theory Martin Genzel
2017	J	`jnl`	Recovering Structured Data From Superimposed Non-Linear Measurements. CoRR Martin Genzel, Peter Jung
2017	J	`jnl`	Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. BMC Bioinform. Tim O. F. Conrad, Martin Genzel, Nada Cvetkovic, Niklas Wulkow, Alexander B. Leichtle, Jan Vybíral, Gitta Kutyniok, Christof Schütte
2016	J	`jnl`	A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations. CoRR Martin Genzel, Gitta Kutyniok
2016	J	`jnl`	High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions. CoRR Martin Genzel
2014	J	`jnl`	Asymptotic Analysis of Inpainting via Universal Shearlet Systems. SIAM J. Imaging Sci. Martin Genzel, Gitta Kutyniok