Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing
datasets provide unprecedented opportunities to dissect cell–cell communication (CCC) …

Optimal transport (OT) has become exceedingly popular in machine learning, data science,
and computer vision. The core assumption in the OT problem is the equal total amount of …

Classical optimal transport problem seeks a transportation map that preserves the total mass
between two probability distributions, requiring their masses to be equal. This may be too …

This work investigates the use of robust optimal transport (OT) for shape matching.
Specifically, we show that recent OT solvers improve both optimization-based and deep …

Optimal transport is a long‐standing theory that has been studied in depth from both
theoretical and numerical point of views. Starting from the 50s this theory has also found a …

Traditional differentiable rendering approaches are usually hard to converge in inverse
rendering optimizations, especially when initial and target object locations are not so close …

Wasserstein distance (WD) and the associated optimal transport plan have been proven
useful in many applications where probability measures are at stake. In this paper, we …

Sliced Wasserstein (SW) distance has been widely used in different application scenarios
since it can be scaled to a large number of supports without suffering from the curse of …

This paper performs comprehensive analysis on datasets for occlusion-aware face
segmentation, a task that is crucial for many downstream applications. The collection and …

This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the
marginal conditions are relaxed (using weighted penalties in lieu of equality) and no …