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2 results for “missing data”
On the missing data layer and a potential solution
A new arXiv preprint identifies a structural gap in Latin America's AI infrastructure—the absence of a coordinated dataset layer—and proposes DataHub, a task-first data infrastructure to improve discovery, contribution, and reuse of regional AI datasets.
Aug 5, 2026
Flow Matching with Missing Data
Researchers introduced Missing-Data Flow Matching, a theoretical and empirical extension of flow matching that rigorously handles incomplete training data by treating missing coordinates as latent variables and proving exact equivalence between incomplete- and complete-data objectives under MCAR assumptions.
Aug 3, 2026