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RNADE: The real-valued neural autoregressive density-estimator
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Paper
RNADE: The real-valued neural autoregressive density-estimator
2014
Overview
We introduce RNADE, a new model for joint density estimation of real-valued vectors. Our model calculates the density of a datapoint as the product of one-dimensional conditionals modeled using mixture density networks with shared parameters. RNADE learns a distributed representation of the data, while having a tractable expression for the calculation of densities. A tractable likelihood allows direct comparison with other methods and training by standard gradient-based optimizers. We compare the performance of RNADE on several datasets of heterogeneous and perceptual data, finding it outperforms mixture models in all but one case.
Publisher
Cornell University Library, arXiv.org
Subject
