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Found 9 papers, 4 papers with code
Parametrizing Product Shape Manifolds by Composite Networks
Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry.
Convergent autoencoder approximation of low bending and low distortion manifold embeddings
The encoder embeds the input data manifold into a lower-dimensional latent space, while the decoder represents the inverse map, providing a parametrization of the data manifold by the manifold in latent space.
Learning low bending and low distortion manifold embeddings
The encoder provides an embedding from the input data manifold into a latent space which may then be used for further processing.
Quartic $L^p$-convergence of cubic Riemannian splines
Still, concepts from the linear setting may be generalized to the Riemannian case, where we try to use intrinsic Riemannian formulations and avoid charts as much as possible.
Numerical Analysis Numerical Analysis 65D10, 65D05
Dynamic Cell Imaging in PET with Optimal Transport Regularization
In contrast to conventional PET reconstruction our method combines the information from all detected events not only to reconstruct the dynamic evolution of the radionuclide distribution, but also to improve the reconstruction at each single time point by enforcing temporal consistency.
A Sinkhorn-Newton method for entropic optimal transport
We further investigate numerically the robustness of the proposed method with respect to parameters such as the mesh size of the discretization.
A Framework for Wasserstein-1-Type Metrics
We propose a unifying framework for generalising the Wasserstein-1 metric to a discrepancy measure between nonnegative measures of different mass.
Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images
Nowadays, modern electron microscopes deliver images at atomic scale.
Vector-valued multibang control of differential equations
We consider a class of (ill-posed) optimal control problems in which a distributed vector-valued control is enforced to pointwise take values in a finite set $\mathcal{M}\subset\mathbb{R}^m$.
Optimization and Control
