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Found 6 papers, 4 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.
Entropic Neural Optimal Transport via Diffusion Processes
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are accessible by samples.
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
Despite the success of WGANs, it is still unclear how well the underlying OT dual solvers approximate the OT cost (Wasserstein-1 distance, $\mathbb{W}_{1}$) and the OT gradient needed to update the generator.
