10 citations · 17 across the 7 of their papers we have counts for
7 papers
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang, Lydia Mezrag, Xingzhi Sun +6
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learnin…
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun, Danqi Liao, Kincaid MacDonald +7
Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it…
A Flow Artist for High-Dimensional Cellular Data
Kincaid MacDonald, Dhananjay Bhaskar, Guy Thampakkul +5
We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapsh…
Learnable Filters for Geometric Scattering Modules
Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar +5
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
Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina +4
We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusi…
Diffusion Earth Mover's Distance and Distribution Embeddings
Alexander Tong, Guillaume Huguet, Amine Natik +5
We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datas…
Data-Driven Learning of Geometric Scattering Networks
Alexander Tong, Frederik Wenkel, Kincaid MacDonald +2
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.…