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Found 8 papers, 5 papers with code
An optimal transport model for dynamical shapes, collective motion and cellular aggregates
Many biological systems such as cell aggregates, tissues or bacterial colonies behave as unconventional systems of particles that are strongly constrained by volume exclusion and shape interactions.
Accurate Point Cloud Registration with Robust Optimal Transport
Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration.
Fast and Scalable Optimal Transport for Brain Tractograms
The parameters -- blur and reach -- of our method are meaningful, defining the minimum and maximum distance at which two fibers are compared with each other.
Fast End-to-End Learning on Protein Surfaces
These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.
Fast geometric learning with symbolic matrices
Geometric methods rely on tensors that can be encoded using a symbolic formula and data arrays, such as kernel and distance matrices.
Kernel Operations on the GPU, with Autodiff, without Memory Overflows
The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices.
Sinkhorn Divergences for Unbalanced Optimal Transport
Optimal transport induces the Earth Mover's (Wasserstein) distance between probability distributions, a geometric divergence that is relevant to a wide range of problems.
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Comparing probability distributions is a fundamental problem in data sciences.
Statistics Theory Statistics Theory 62
