3 citations · 3 across the 1 of their papers we have counts for
3 papers
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs
Martin Hanik, Gabriele Steidl, Christoph von Tycowicz
We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion l…
p-Laplacians for Manifold-valued Hypergraphs
Jo Andersson Stokke, Ronny Bergmann, Martin Hanik +1
Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we d…
Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative
Johannes Schade, Christoph von Tycowicz, Martin Hanik
Many phenomena are naturally characterized by measuring continuous transformations such as shape changes in medicine or articulated systems in robotics. Modeling the variability in…