mlsanityData to 7 Oct 2026

Masked Autoregressive Flow for Density Estimation

arXiv 1705.07057 ↗First posted 19 May 2017stat.ML cs.LGcited by 680

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Cited by 40 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 680 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2017.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 132; 2022: 125; 2023: 129; 2024: 106; 2025: 103; 2026: 83.

680 papers.

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