awesome-normalizing-flows
Awesome resources on normalizing flows. (by janosh)
vbmc
Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB (by acerbilab)
| awesome-normalizing-flows | vbmc | |
|---|---|---|
| 1 | 2 | |
| 1,313 | 211 | |
| - | 1.9% | |
| 3.6 | 2.8 | |
| about 1 month ago | about 1 year ago | |
| Python | MATLAB | |
| MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-normalizing-flows
Posts with mentions or reviews of awesome-normalizing-flows.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[D] Understanding Generative Flow
I would recommend this list of resources on github to get you started. In particular, I highly recommend this lecture by Marcus Brubaker et al which explains the essential components that you need: linear transformations, coupling layers and the multiscale architecture.
vbmc
Posts with mentions or reviews of vbmc.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-05.
-
[R] New open-source Python software for sample-efficient Bayesian inference
Relevant papers about the underlying algorithm were published at NeurIPS in 2018 and 2020, but this is the first Python implementation (there was a MATLAB implementation); the port took us a while but it can finally be used for machine learning purposes
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New Python open-source software for sample-efficient Bayesian inference
The MATLAB implementation has been around for a while, and other research groups have applied it among other things for Bayesian parameter inference in:
What are some alternatives?
When comparing awesome-normalizing-flows and vbmc you can also consider the following projects:
PyMC - Bayesian Modeling and Probabilistic Programming in Python
pyro - Deep universal probabilistic programming with Python and PyTorch
nflows - Normalizing flows in PyTorch
GPflow - Gaussian processes in TensorFlow
autoregressive - :kiwi_fruit: Autoregressive Models in PyTorch.
PRMLT - Matlab code of machine learning algorithms in book PRML
InvertibleNetworks.jl - A Julia framework for invertible neural networks
MATLAB-Guide - MATLAB Guide
Tensorflow-iOS
FraudDetection - Accounting Fraud Detection Using Machine Learning
vbmc - Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB (old location)
awesome-normalizing-flows vs PyMC
vbmc vs pyro
awesome-normalizing-flows vs nflows
vbmc vs GPflow
awesome-normalizing-flows vs autoregressive
vbmc vs PRMLT
awesome-normalizing-flows vs InvertibleNetworks.jl
vbmc vs MATLAB-Guide
awesome-normalizing-flows vs Tensorflow-iOS
vbmc vs FraudDetection
awesome-normalizing-flows vs pyro
vbmc vs vbmc