44 citations · 253 across the 39 of their papers we have counts for
9 papers · 1 filter
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds
Guillaume Huguet, Alexander Tong, María Ramos Zapatero +3
Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods are currently the state-of-the-art for su…
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Chen Liu, Matthew Amodio, Liangbo L. Shen +6
Segmenting medical images is critical to facilitating both patient diagnoses and quantitative research. A major limiting factor is the lack of labeled data, as obtaining expert ann…
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.…
Geometric Scattering on Measure Spaces
Joyce Chew, Matthew Hirn, Smita Krishnaswamy +5
The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in o…
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…
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Guillaume Huguet, D. S. Magruder, Alexander Tong +4
We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at spor…