Entropic (Gromov) Wasserstein Flow Matching with GENOT

arXiv:2310.09254v3 Announce Type: replace-cross
Abstract: Optimal transport (OT) theory has reshaped the field of generative modeling: Combined with neural networks, recent \textit{Neural OT} (N-OT) solvers use OT as an inductive bias, to focus on ``thrifty'' mappings that minimize average displacement costs. This core principle has fueled the successful application of N-OT solvers to high-stakes scientific challenges, notably single-cell genomics. N-OT solvers are, however, increasingly confronted with practical challenges: while most N-OT solvers can handle squared-Euclidean costs, they must be repurposed …

abstract application arxiv bias core costs cs.lg flow focus generative generative modeling inductive modeling networks neural networks stat.ml theory transport type