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LINFA: a Python library for variational inference with normalizing flow and annealing

arXiv 2307.04675 ↗First posted 10 Jul 2023cs.LG stat.COcited by 0

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Abstract (arXiv)

Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational inference to accommodate computationally expensive models and difficult-to-sample distributions with dependent parameters. We discuss the theoretical background, capabilities, and performance of LINFA in various benchmarks. LINFA is publicly available on GitHub at https://github.com/desResLab/LINFA.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 10 Jul 2023.

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References · 15

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2015Variational Inference with Normalizing Flowscited by 2,170
  3. 2016Density estimation using Real NVPcited by 2,042
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  6. 2017Masked Autoregressive Flow for Density Estimationcited by 684
  7. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 640
  8. 2018Pyro: Deep Universal Probabilistic Programmingcited by 380
  9. 2015MADE: Masked Autoencoder for Distribution Estimationcited by 307
  10. 2021Preconditioned training of normalizing flows for variational inference in inverse problemscited by 20
  11. 2021Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Modelscited by 7
  12. 2023PyVBMC: Efficient Bayesian inference in Pythoncited by 7
3 more
  1. 2014BayesPy: Variational Bayesian Inference in Pythoncited by 4
  2. 2022AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximationcited by 3
  3. 2023Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Recordscited by 2

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