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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
I built my 'first' flow matching image generator, here's what I learned [P]
An individual developer built a small-scale, educational flow-matching image generator using Apple emoji data and publicly available tools, documenting technical learnings from iterative model design on consumer hardware.
Published Jul 4, 2026 · Analyzed Jul 6, 2026