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Found 6 papers, 3 papers with code
Estimating Barycenters of Distributions with Neural Optimal Transport
Given a collection of probability measures, a practitioner sometimes needs to find an "average" distribution which adequately aggregates reference distributions.
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties.
Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
We fill this gap and propose a novel way to create pairs of probability distributions for which the ground truth OT solution is known by the construction.
Energy-guided Entropic Neural Optimal Transport
Energy-based models (EBMs) are known in the Machine Learning community for decades.
Neural Optimal Transport with General Cost Functionals
We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals.
Large-Scale Wasserstein Gradient Flows
Specifically, Fokker-Planck equations, which model the diffusion of probability measures, can be understood as gradient descent over entropy functionals in Wasserstein space.
