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cs.LG2024★ 3 cited
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…
cs.LG2021★ 17 cited
Predicting cognitive scores with graph neural networks through sample selection learning
Martin Hanik, Mehmet Arif Demirtaş, Mohammed Amine Gharsallaoui +1
Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In exi…