Masked Autoregressive Flow for Density Estimation

George Papamakarios, Theo Pavlakou, Iain Murray

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


Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow. This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 30
EditorsI. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett
Place of PublicationLong Beach, United States
PublisherCurran Associates Inc
Number of pages10
Publication statusPublished - 9 Dec 2017
EventThirty-first Annual Conference on Neural Information Processing Systems - Long Beach Convention Center, Long Beach, United States
Duration: 4 Dec 20179 Dec 2017

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Electronic)1049-5258


ConferenceThirty-first Annual Conference on Neural Information Processing Systems
Abbreviated titleNIPS
Country/TerritoryUnited States
CityLong Beach


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