Martin Genzel | DeepAI

DeepAI AI Chat

Featured Co-authors

Gitta Kutyniok
53 publications
Peter Jung
39 publications
Laurent Jacques
39 publications
Sjoerd Dirksen
14 publications
Christof Schütte
12 publications
Jan Macdonald
10 publications
Jan Vybiral
9 publications
Maximilian März
8 publications
Alexander Stollenwerk
7 publications
Julien Siems
6 publications
Maximilian Schambach
5 publications

∙ 05/19/2023

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models

Generalized Additive Models (GAMs) have recently experienced a resurgenc...

0 Julien Siems, et al. ∙

∙ 11/18/2022

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

This work presents a novel deep-learning-based pipeline for the inverse ...

0 Theophil Trippe, et al. ∙

∙ 02/07/2022

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

Countless signal processing applications include the reconstruction of s...

11 Jonathan Sauder, et al. ∙

∙ 07/31/2021

The Separation Capacity of Random Neural Networks

Neural networks with random weights appear in a variety of machine learn...

0 Sjoerd Dirksen, et al. ∙

∙ 06/01/2021

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

This report is dedicated to a short motivation and description of our co...

0 Martin Genzel, et al. ∙

∙ 11/09/2020

Solving Inverse Problems With Deep Neural Networks – Robustness Included?

In the past five years, deep learning methods have become state-of-the-a...

18 Martin Genzel, et al. ∙

∙ 09/19/2020

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

Recent advances in quantized compressed sensing and high-dimensional est...

0 Martin Genzel, et al. ∙

∙ 09/07/2020

Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20)

This paper investigates total variation minimization in one spatial dime...

0 Martin Genzel, et al. ∙

∙ 04/11/2020

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

This work performs a non-asymptotic analysis of the (constrained) genera...

0 Martin Genzel, et al. ∙

∙ 01/27/2020

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

This paper investigates total variation minimization in one spatial dime...

0 Martin Genzel, et al. ∙

∙ 08/20/2018

The Mismatch Principle: Statistical Learning Under Large Model Uncertainties

We study the learning capacity of empirical risk minimization with regar...

0 Martin Genzel, et al. ∙

∙ 04/13/2018

Robust 1-Bit Compressed Sensing via Hinge Loss Minimization

This work theoretically studies the problem of estimating a structured h...

0 Martin Genzel, et al. ∙

∙ 08/31/2016

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

In this paper, we study the challenge of feature selection based on a re...

0 Martin Genzel, et al. ∙

∙ 06/11/2015

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

Background: High-throughput proteomics techniques, such as mass spectrom...

0 Tim Conrad, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world