A Deep and Tractable Density Estimator

Benigno Uria, Iain Murray, Hugo Larochelle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The Neural Autoregressive Distribution Estimator (NADE) and its real-valued version RNADE are competitive density models of multidimensional data across a variety of domains. These models use a fixed, arbitrary ordering of the data dimensions. One can easily condition on variables at the beginning of the ordering, and marginalize out variables at the end of the ordering, however other inference tasks require approximate inference. In this work we introduce an efficient procedure to simultaneously train a NADE model for each possible ordering of the variables, by sharing parameters across all these models. We can thus use the most convenient model for each inference task at hand, and ensembles of such models with different orderings are immediately available. Moreover, unlike the original NADE, our training procedure scales to deep models. Empirically, ensembles of Deep NADE models obtain state of the art density estimation performance.
Original languageEnglish
Title of host publicationProceedings of the 31st International Conference on Machine Learning
Number of pages9
Publication statusPublished - 24 Jun 2014
Event31st International Conference on International Conference on Machine Learning - Beijing, China
Duration: 21 Jun 201426 Jun 2014

Publication series

NameJournal of Machine Learning Research: Workshops & Conference Proceedings
ISSN (Electronic)1938-7228


Conference31st International Conference on International Conference on Machine Learning
Abbreviated titleICML'14
Internet address


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