mlsanityData to 7 Oct 2026

normflows: A PyTorch Package for Normalizing Flows

arXiv 2302.12014 ↗First posted 26 Jan 2023cs.LGcited by 56

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Abstract (arXiv)

Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation, text modeling, variational inference, approximating Boltzmann distributions, and many other problems. Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch, which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP, Glow, Masked Autoregressive Flows, Neural Spline Flows, Residual Flows, and many more. The package can be easily installed via pip and the code is publicly available on GitHub.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

18 of 28 references.

References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 2016Density estimation using Real NVPcited by 2,029
  5. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  6. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  7. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  8. 2019Neural Spline Flowscited by 590
  9. 2019Residual Flows for Invertible Generative Modelingcited by 256
  10. 2020Stochastic Normalizing Flowscited by 156
  11. 2018Neural Importance Samplingcited by 154
  12. 2022Flow Annealed Importance Sampling Bootstrapcited by 124
6 more
  1. 2020Normalizing Flows on Tori and Spherescited by 117
  2. 2017TensorFlow Distributionscited by 105
  3. 2020SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flowscited by 71
  4. 2021Densely connected normalizing flowscited by 49
  5. 2021Resampling Base Distributions of Normalizing Flowscited by 42
  6. 2019Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modelingcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 13; 2024: 20; 2025: 17; 2026: 6.

56 papers.

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  1. 2024Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flowcited by 25
  2. 2024Mutual Information Estimation via Normalizing Flowscited by 22
  3. 2024Is Tokenization Needed for Masked Particle Modelling?cited by 20
  4. 2024Neural Conditional Probability for Uncertainty Quantificationcited by 14
  5. 2023Squared Neural Families: A New Class of Tractable Density Modelscited by 12
  6. 2025Improving the evaluation of samplers on multi-modal targetscited by 9
  7. 2023Deep Backtracking Counterfactuals for Causally Compliant Explanationscited by 8
  8. 2023Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesiscited by 7
  9. 2023Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learningcited by 7
  10. 2024Neural-Kernel Conditional Mean Embeddingscited by 7
  11. 2024Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modelingcited by 7
  12. 2025InfoBridge: Mutual Information estimation via Bridge Matchingcited by 7
38 more
  1. 2025Simplifying Bayesian Optimization Via In-Context Direct Optimum Samplingcited by 7
  2. 2023InvertibleNetworks.jl: A Julia package for scalable normalizing flowscited by 6
  3. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  4. 2024Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representationscited by 5
  5. 2024Stable Training of Normalizing Flows for High-dimensional Variational Inferencecited by 5
  6. 2023One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Modelscited by 4
  7. 2023LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spacescited by 3
  8. 2025Flows on convex polytopescited by 3
  9. 2025Learning Geometry and Topology via Multi-Chart Flowscited by 3
  10. 2023Some challenges of calibrating differentiable agent-based modelscited by 2
  11. 2023Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assemblycited by 2
  12. 2023Learning to Transform for Generalizable Instance-wise Invariancecited by 2
  13. 2024REMEDI: Corrective Transformations for Improved Neural Entropy Estimationcited by 2
  14. 2024Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flowscited by 2
  15. 2024Base Models for Parabolic Partial Differential Equationscited by 2
  16. 2024Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levelscited by 2
  17. 2024Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical losscited by 2
  18. 2024Conformal Calibration of Statistical Confidence Setscited by 2
  19. 2025Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolvecited by 2
  20. 2025Mamba time series forecasting with uncertainty quantificationcited by 2
  21. 2025Federated Conditional Conformal Prediction via Generative Modelscited by 2
  22. 2023Testable Likelihoods for Beyond-the-Standard Model Fitscited by 1
  23. 2023It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimationcited by 1
  24. 2024Injective flows for star-like manifoldscited by 1
  25. 2024Preferential Normalizing Flowscited by 1
  26. 2025NeuMC -- a package for neural sampling for lattice field theoriescited by 1
  27. 2025Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergencecited by 1
  28. 2025VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillationcited by 1
  29. 2026Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problemscited by 1
  30. 2024Improving Lambda Signal Extraction with Domain Adaptation via Normalizing Flowscited by 0
  31. 2024An AI-powered Technology Stack for Solving Many-Electron Field Theorycited by 0
  32. 2024Identifying latent state transition in non-linear dynamical systemscited by 0
  33. 2024Density Estimation via Binless Multidimensional Integrationcited by 0
  34. 2025Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division historycited by 0
  35. 2025On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditionscited by 0
  36. 2025Stick-Breaking Mixture Normalizing Flows with Component-Wise Tail Adaptation for Variational Inferencecited by 0
  37. 2025A Likely Geometry of Generative Modelscited by 0
  38. 2025Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filterscited by 0

50 most cited of 56 All 56 (JSON)