51 citations · 191 across the 30 of their papers we have counts for
35 papers
Parametrizing Product Shape Manifolds by Composite Networks
Josua Sassen, Klaus Hildebrandt, Martin Rumpf +1
Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry. This, however, often comes with high computational costs, which ra…
Convergent autoencoder approximation of low bending and low distortion manifold embeddings
Juliane Braunsmann, Marko Rajković, Martin Rumpf +1
Autoencoders, which consist of an encoder and a decoder, are widely used in machine learning for dimension reduction of high-dimensional data. The encoder embeds the input data man…
Exact reconstruction and reconstruction from noisy data with anisotropic total variation
Martin Holler, Benedikt Wirth
It is well-known that point sources with sufficient mutual distance can be reconstructed exactly from finitely many Fourier measurements by solving a convex optimization problem wi…
A non-convex variational model for joint polyenergetic CT reconstruction, sensor denoising and material decomposition
Georgios Papanikos, Benedikt Wirth
Computed Tomography (CT) is widely used in engineering and medicine for imaging the interior of objects, patients, or animals. If the employed X-ray source is monoenergetic, image…
Duality in branched transport and urban planning
Julius Lohmann, Bernhard Schmitzer, Benedikt Wirth
In recent work arXiv:2109.07820 we have shown the equivalence of the widely used nonconvex (generalized) branched transport problem with a shape optimization problem of a street or…
Dimension reduction, exact recovery, and error estimates for sparse reconstruction in phase space
Martin Holler, Alexander Schlüter, Benedikt Wirth
An important theme in modern inverse problems is the reconstruction of time-dependent data from only finitely many measurements. To obtain satisfactory reconstruction results in th…