mlsanityData to 7 Oct 2026

A Deep and Tractable Density Estimator

arXiv 1310.1757 ↗First posted 7 Oct 2013stat.ML cs.LGcited by 67

Not on the map. Why?

Cited by 28 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2013.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 5; 2024: 9; 2025: 20; 2026: 17.

67 papers.

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  12. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
38 more
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  14. 2025Error Bounds and Optimal Schedules for Masked Diffusions with Factorized Approximationscited by 13
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  16. 2025dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learningcited by 10
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