mlsanityData to 7 Oct 2026

Sylvester Normalizing Flows for Variational Inference

arXiv 1803.05649 ↗First posted 15 Mar 2018stat.ML cs.AI cs.LGcited by 71

Not on the map. Why?

Abstract (arXiv)

Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 26; 2022: 15; 2023: 12; 2024: 8; 2025: 8; 2026: 2.

71 papers.

Sort:

By area:

  1. 2024Parallelizing Linear Transformers with the Delta Rule over Sequence Lengthcited by 373
  2. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  3. 2022DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latentscited by 103
  4. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 99
  5. 2022Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantificationcited by 71
  6. 2021A Dynamical System Perspective for Lipschitz Neural Networkscited by 59
  7. 2023Geometric Clifford Algebra Networkscited by 37
  8. 2023Explicitly Minimizing the Blur Error of Variational Autoencoderscited by 29
  9. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 28
  10. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  11. 2021MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retrainingcited by 23
  12. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
38 more
  1. 2021Invertible DenseNets with Concatenated LipSwishcited by 17
  2. 2021PU-Flow: a Point Cloud Upsampling Network with Normalizing Flowscited by 17
  3. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  4. 2022Flow-based Recurrent Belief State Learning for POMDPscited by 16
  5. 2022Invertible Rescaling Network and Its Extensionscited by 15
  6. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  7. 2021Cauchy-Schwarz Regularized Autoencodercited by 13
  8. 2021Sparse Flows: Pruning Continuous-depth Modelscited by 13
  9. 2021Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flowscited by 13
  10. 2024Variational Bayesian Optimal Experimental Design with Normalizing Flowscited by 13
  11. 2021On Incorporating Inductive Biases into VAEscited by 11
  12. 2024EigenVI: score-based variational inference with orthogonal function expansionscited by 11
  13. 2021Model Selection for Bayesian Autoencoderscited by 10
  14. 2022Path-Gradient Estimators for Continuous Normalizing Flowscited by 10
  15. 2021Jacobian Determinant of Normalizing Flowscited by 8
  16. 2025An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architecturescited by 8
  17. 2021Structured Dropout Variational Inference for Bayesian Neural Networkscited by 7
  18. 2021Universal Approximation of Residual Flows in Maximum Mean Discrepancycited by 7
  19. 2021Robust normalizing flows using Bernstein-type polynomialscited by 6
  20. 2023Embracing the chaos: analysis and diagnosis of numerical instability in variational flowscited by 6
  21. 2021Bootstrap Your Flowcited by 5
  22. 2023GC-Flow: A Graph-Based Flow Network for Effective Clusteringcited by 5
  23. 2023Conditional Matrix Flows for Gaussian Graphical Modelscited by 5
  24. 2025LLM-Prior: A Framework for Knowledge-Driven Prior Elicitation and Aggregationcited by 5
  25. 2021Augmented KRnet for density estimation and approximationcited by 4
  26. 2021Distilling the Knowledge from Conditional Normalizing Flowscited by 4
  27. 2021On the Generative Utility of Cyclic Conditionalscited by 4
  28. 2022Principal Manifold Flowscited by 4
  29. 2022CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrationscited by 4
  30. 2022Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environmentscited by 4
  31. 2023Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical datacited by 4
  32. 2023One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Modelscited by 4
  33. 2021Modeling Item Response Theory with Stochastic Variational Inferencecited by 3
  34. 2022Bernstein Flows for Flexible Posteriors in Variational Bayescited by 3
  35. 2022Statistical Model Criticism of Variational Auto-Encoderscited by 3
  36. 2022Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Studycited by 3
  37. 2025Generative Artificial Intelligence for Internet of Things Computing: A Systematic Surveycited by 3
  38. 2022Universality of parametric Coupling Flows over parametric diffeomorphismscited by 2

50 most cited of 71 All 71 (JSON)