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University of Padova
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AM generates smooth trajectories efficiently, sidestepping costly preprocessing and simulation, while achieving competitive results against existing methods.
Improved KL and Wasserstein convergence bounds for Diffusion Flow Matching reveal state-of-the-art performance in high dimensions under minimal assumptions.
