Diffusion and flow processes forget dependencies to define valid copulas then learn to remember them for density estimation and sampling, outperforming prior copula methods on complex datasets.
(2023).Towards a universal repre- sentation of statistical dependence
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Deep neural networks trained to classify simulated samples under null and alternative hypotheses produce a test statistic that outperforms nineteen competing methods for independence testing across varied dependence structures.
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Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies
Diffusion and flow processes forget dependencies to define valid copulas then learn to remember them for density estimation and sampling, outperforming prior copula methods on complex datasets.
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Deep-testing: the case of dependence detection
Deep neural networks trained to classify simulated samples under null and alternative hypotheses produce a test statistic that outperforms nineteen competing methods for independence testing across varied dependence structures.