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Found 5 papers, 1 papers with code
A Flow Artist for High-Dimensional Cellular Data
Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i. e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field.
Learnable Filters for Geometric Scattering Modules
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature.
Diffusion Earth Mover's Distance and Distribution Embeddings
Here, Diffusion EMD can derive distances between patients on the manifold of cells at least two orders of magnitude faster than equally accurate methods.
Data-Driven Learning of Geometric Scattering Networks
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
