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Found 5 papers, 3 papers with code
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs
We propose two graph neural network layers for graphs with features in a Riemannian manifold.
Intrinsic shape analysis in archaeology: A case study on ancient sundials
By performing regression in shape space, we find that for Roman sundials, the bend of the sundials' shadow-receiving surface changes with the location's latitude.
Sasaki Metric for Spline Models of Manifold-Valued Trajectories
We propose a generic spatiotemporal framework to analyze manifold-valued measurements, which allows for employing an intrinsic and computationally efficient Riemannian hierarchical model.
Predicting Shape Development: a Riemannian Method
Predicting the future development of an anatomical shape from a single baseline observation is a challenging task.
Predicting cognitive scores with graph neural networks through sample selection learning
On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task.
