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The key ingredient is that, for a constructed jump bridge, a parametrization of the jump kernel densities by scaled Gaussians admits closed-form expressions for the Kullback-Leibler divergence, allowing simulation-free training.
This paper introduces a distance measure that assesses the output of material generative models by capturing both quality and novelty in a single distribution-based evaluation framework, and introduces the Coarse-Fine Transport Distance (CFTD), which is used as guidance for a material generative model.
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