Abstract:Estimation of probability density function from samples is one of the central problems in statistics and machine learning. Modern neural networkbased models can learn high dimensional distributions but have problems with hyperparameter selection and are often prone to instabilities during training and inference. We propose a new efficient tensor train-based model for density estimation (TTDE). Such density parametrization allows exact sampling, calculation of cumulative and marginal density functions, and part… Show more
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