Geometric Deep Learning extends deep learning to non-Euclidean structures, which might be graphs, point clouds or others. From the structure of the data naturally arise symmetries, that can be exploited to improve model performance or enhance generalization capabilities. We will study some of those methods with a special focus on graph neural networks (GNNs) that respect rotation and translation symmetries.
- Trainer/in: Jacob Reinhard Kauffmann
- Trainer/in: Winfried Ripken