March 14, 2024, 4:43 a.m. | Dominik Klein, Th\'eo Uscidda, Fabian Theis, Marco Cuturi

cs.LG updates on arXiv.org arxiv.org

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

Senior Data Engineer

@ Displate | Warsaw

Junior Data Analyst - ESG Data

@ Institutional Shareholder Services | Mumbai

Intern Data Driven Development in Sensor Fusion for Autonomous Driving (f/m/x)

@ BMW Group | Munich, DE

Senior MLOps Engineer, Machine Learning Platform

@ GetYourGuide | Berlin

Data Engineer, Analytics

@ Meta | Menlo Park, CA

Data Engineer

@ Meta | Menlo Park, CA