1 citations · 1 across the 3 of their papers we have counts for
7 papers
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Sebastian Neumayer, Alexis Goujon, Pakshal Bohra +1
Lipschitz-constrained neural networks have many applications in machine learning. Since designing and training expressive Lipschitz-constrained networks is very challenging, there…
An Image Registration Model in Electron Backscatter Diffraction
Manuel Gräf, Sebastian Neumayer, Ralf Hielscher +3
Recently, variational methods were successfully applied for computing the optical flow in gray and RGB-valued image sequences. A crucial assumption in these models is that pixel-va…
From Optimal Transport to Discrepancy
Sebastian Neumayer, Gabriele Steidl
A common way to quantify the ,,distance'' between measures is via their discrepancy, also known as maximum mean discrepancy (MMD). Discrepancies are related to Sinkhorn divergences…
Parseval Proximal Neural Networks
Marzieh Hasannasab, Johannes Hertrich, Sebastian Neumayer +3
The aim of this paper is twofold. First, we show that a certain concatenation of a proximity operator with an affine operator is again a proximity operator on a suitable Hilbert sp…
Frame Soft Shrinkage as Proximity Operator
Marzieh Hassanasab, Sebastian Neumayer, Gerlind Plonka +3
Let and be real Hilbert spaces and an injective operator with closed range and Moore-Penrose inverse $T^\dagge…
Curve Based Approximation of Measures on Manifolds by Discrepancy Minimization
Martin Ehler, Manuel Gräf, Sebastian Neumayer +1
The approximation of probability measures on compact metric spaces and in particular on Riemannian manifoldsby atomic or empirical ones is a classical task in approximation and com…