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Neural Autoregressive Distribution Estimation

arXiv 1605.02226 ↗First posted 7 May 2016cs.LGcited by 95

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Abstract (arXiv)

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density estimation. They leverage the probability product rule and a weight sharing scheme inspired from restricted Boltzmann machines, to yield an estimator that is both tractable and has good generalization performance. We discuss how they achieve competitive performance in modeling both binary and real-valued observations. We also present how deep NADE models can be trained to be agnostic to the ordering of input dimensions used by the autoregressive product rule decomposition. Finally, we also show how to exploit the topological structure of pixels in images using a deep convolutional architecture for NADE.

Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 May 2016.

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Citing papers per year: 2021: 22; 2022: 13; 2023: 15; 2024: 20; 2025: 15; 2026: 10.

95 papers.

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  2. 2024Randomized Autoregressive Visual Generationcited by 127
  3. 2021ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesiscited by 122
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  10. 2023One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Eracited by 55
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  12. 2022UniMASK: Unified Inference in Sequential Decision Problemscited by 24
38 more
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  4. 2021Autoregressive neural-network wavefunctions for ab initio quantum chemistrycited by 21
  5. 2025Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimizationcited by 20
  6. 2021Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networkscited by 17
  7. 2023Diffusion Models for High-Resolution Solar Forecastscited by 17
  8. 2025Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecturecited by 16
  9. 2021Discovering Non-monotonic Autoregressive Orderings with Variational Inferencecited by 15
  10. 2023A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Themcited by 15
  11. 2021HyperSPNs: Compact and Expressive Probabilistic Circuitscited by 14
  12. 2024A theoretical perspective on mode collapse in variational inferencecited by 14
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  14. 2021Tensor networks and efficient descriptions of classical datacited by 13
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  19. 2021Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twittercited by 10
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  22. 2023Generative Modeling via Hierarchical Tensor Sketchingcited by 8
  23. 2024A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle acceleratorscited by 8
  24. 2021Probabilistic Time Series Forecasts with Autoregressive Transformation Modelscited by 7
  25. 2023SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecastingcited by 7
  26. 2024Conditional diffusions for amortized neural posterior estimationcited by 7
  27. 2024Message Passing Variational Autoregressive Network for Solving Intractable Ising Modelscited by 6
  28. 2021The DEformer: An Order-Agnostic Distribution Estimating Transformercited by 5
  29. 2021A Bayesian generative neural network framework for epidemic inference problemscited by 5
  30. 2025BARNN: A Bayesian Autoregressive and Recurrent Neural Networkcited by 5
  31. 2021Scaling of neural-network quantum states for time evolutioncited by 4
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  33. 2023Mutual information of spin systems from autoregressive neural networkscited by 4
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  35. 2024A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Researchcited by 4
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  37. 2021Composable Generative Modelscited by 3
  38. 2021F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuitscited by 3

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