nips nips2013 nips2013-260 nips2013-260-reference knowledge-graph by maker-knowledge-mining
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Author: Benigno Uria, Iain Murray, Hugo Larochelle
Abstract: 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 onedimensional 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 gradientbased 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. 1