AINeutralarXiv – CS AI · 7h ago6/10
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Active Timepoint Selection for Learning Measure-Valued Trajectories
Researchers introduce an active learning framework for inferring continuous probability distributions from sparse data snapshots, addressing a key challenge in fields like single-cell biology where data collection is destructive and expensive. The method uses Linearized Optimal Transport to map probability distributions into a space suitable for Gaussian Process modeling, enabling uncertainty-guided selection of optimal measurement times.