Search Results for author:
Found 2 papers, 2 papers with code
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Finding meaningful representations and distances of hierarchical data is important in many fields.
Hyperbolic Procrustes Analysis Using Riemannian Geometry
Here, we take a purely geometric approach for label-free alignment of hierarchical datasets and introduce hyperbolic Procrustes analysis (HPA).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
