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Found 4 papers, 3 papers with code
Example-Based Sampling with Diffusion Models
Much effort has been put into developing samplers with specific properties, such as producing blue noise, low-discrepancy, lattice or Poisson disk samples.
Interpolated corrected curvature measures for polygonal surfaces
We pro- pose a new framework to define curvature measures, based on the Corrected Normal Current, which generalizes the normal cycle: it uncouples the positional information of the polyhedral mesh from its geometric normal vector field, and the user can freely choose the corrected normal vector field at vertices for curvature computations.
Ground Metric Learning on Graphs
Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations.
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
Wasserstein barycenters) between dictionary atoms; such atoms are themselves synthetic histograms in the probability simplex.
