mlsanityData to 7 Oct 2026

TensorFlow Distributions

arXiv 1711.10604 ↗First posted 28 Nov 2017cs.LG cs.AI cs.PLcited by 105

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Abstract (arXiv)

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors provide composable volume-tracking transformations with automatic caching. Together these enable modular construction of high dimensional distributions and transformations not possible with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible residual networks). They are the workhorse behind deep probabilistic programming systems like Edward and empower fast black-box inference in probabilistic models built on deep-network components. TensorFlow Distributions has proven an important part of the TensorFlow toolkit within Google and in the broader deep learning community.

Key facts. Cited by 105 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Nov 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 21; 2022: 21; 2023: 25; 2024: 17; 2025: 14; 2026: 7.

105 papers.

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  1. 2021Annealed Flow Transport Monte Carlocited by 90
  2. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
  3. 2023normflows: A PyTorch Package for Normalizing Flowscited by 56
  4. 2021Copula Flows for Synthetic Data Generationcited by 35
  5. 2023BayesFlow: Amortized Bayesian Workflows With Neural Networkscited by 32
  6. 2021Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priorscited by 29
  7. 2023L-HYDRA: Multi-Head Physics-Informed Neural Networkscited by 29
  8. 2021Pathfinder: Parallel quasi-Newton variational inferencecited by 26
  9. 2023A Primer on Bayesian Neural Networks: Review and Debatescited by 26
  10. 2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
  11. 2023On the Convergence of Black-Box Variational Inferencecited by 22
  12. 2023Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialscited by 16
38 more
  1. 2022Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flowscited by 14
  2. 2024The Vizier Gaussian Process Bandit Algorithmcited by 14
  3. 2021Conditional Variational Autoencoder for Learned Image Reconstructioncited by 13
  4. 2021GPflux: A Library for Deep Gaussian Processescited by 12
  5. 2021Structured Stochastic Gradient MCMCcited by 12
  6. 2021M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyondcited by 11
  7. 2022Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicscited by 11
  8. 2021Principal Component Density Estimation for Scenario Generation Using Normalizing Flowscited by 10
  9. 2021Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)cited by 9
  10. 2023Testing for the Markov Property in Time Series via Deep Conditional Generative Learningcited by 9
  11. 2022Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transportcited by 8
  12. 2022Learning Robust Dynamics through Variational Sparse Gatingcited by 8
  13. 2022Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detectioncited by 7
  14. 2023On the Properties and Estimation of Pointwise Mutual Information Profilescited by 7
  15. 2024A Comprehensive Guide to Explainable AI: From Classical Models to LLMscited by 7
  16. 2021A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modelingcited by 6
  17. 2023Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inferencecited by 6
  18. 2024Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagerycited by 6
  19. 2022MARS: Meta-Learning as Score Matching in the Function Spacecited by 5
  20. 2022An Empirical Bayes Analysis of Object Trajectory Representation Modelscited by 5
  21. 2023Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibrationcited by 5
  22. 2024Simulation-based Inference with the Python Package sbijaxcited by 5
  23. 2021Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Modelscited by 4
  24. 2021An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applicationscited by 4
  25. 2022A Survey on Uncertainty Toolkits for Deep Learningcited by 4
  26. 2022Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regressioncited by 4
  27. 2023ShaRP: Shape-Regularized Multidimensional Projectionscited by 4
  28. 2023Self-Tuning Hamiltonian Monte Carlo for Accelerated Samplingcited by 4
  29. 2024Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantificationcited by 4
  30. 2021Calibrated simplex-mapping classificationcited by 3
  31. 2021ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Modelscited by 3
  32. 2022Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometrycited by 3
  33. 2023Logistic-Normal Likelihoods for Heteroscedastic Label Noisecited by 3
  34. 2023Preference Elicitation with Soft Attributes in Interactive Recommendationcited by 3
  35. 2025Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capturecited by 3
  36. 2025Incorporating the ChEES Criterion into Sequential Monte Carlo Samplerscited by 3
  37. 2021BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statisticscited by 2
  38. 2022Scalable Semi-Modular Inference with Variational Meta-Posteriorscited by 2

50 most cited of 105 All 105 (JSON)