WassersteinIterativeNetworks forBarycenterEstimation
–Neural Information Processing Systems
Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way. In this paper, we present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model.
Neural Information Processing Systems
Feb-9-2026, 11:12:27 GMT
- Country:
- Asia > Russia (0.05)
- North America > United States
- California (0.04)
- Massachusetts > Middlesex County
- Cambridge (0.14)
- Europe > Russia
- Central Federal District > Moscow Oblast > Moscow (0.05)
- Technology: