Search Results for author: Ingo Gühring

Found 5 papers, 1 papers with code

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

1 code implementation14 Jun 2022 Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy?

Computed Tomography (CT)

Near-Exact Recovery for Sparse-View CT via Data-Driven Methods

no code implementations NeurIPS Workshop Deep_Invers 2021 Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic measurements with unknown geometry.

Image Reconstruction

A neural multilevel method for high-dimensional parametric PDEs

no code implementations NeurIPS Workshop DLDE 2021 Cosmas Heiß, Ingo Gühring, Martin Eigel

In scientific machine learning, neural networks recently have become a popular tool for learning the solutions of differential equations.

Expressivity of Deep Neural Networks

no code implementations9 Jul 2020 Ingo Gühring, Mones Raslan, Gitta Kutyniok

In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks.

Error bounds for approximations with deep ReLU neural networks in $W^{s,p}$ norms

no code implementations21 Feb 2019 Ingo Gühring, Gitta Kutyniok, Philipp Petersen

We analyze approximation rates of deep ReLU neural networks for Sobolev-regular functions with respect to weaker Sobolev norms.

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