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Found 9 papers, 7 papers with code
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
To the best of our knowledge, this paper is the first attempt to develop an algorithm for robust barycenters under the continuous distribution setup.
Optimal Flow Matching: Learning Straight Trajectories in Just One Step
Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling.
Estimating Barycenters of Distributions with Neural Optimal Transport
A theoretically appealing notion of such an average is the Wasserstein barycenter, which is the primal focus of our work.
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.
Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem
In the discrete variant of GWOT, the task is to learn an assignment between given discrete sets of points.
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.
