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This paper introduces a framework for active timepoint selection to infer probability paths from sparse snapshots, using linearized optimal transport to map distributions into a tangent space for Gaussian Process modeling, thereby enabling uncertainty-aware acquisition policies.
This paper proposes a unified framework for energy-based generative models by casting density transport as a nonlinear control problem with KL divergence as a Lyapunov function. It derives finite-step stopping criteria and demonstrates how nonlinear control theory tools can be applied to static scalar energy models.