awesome-normalizing-flows VS InvertibleNetworks.jl

Compare awesome-normalizing-flows vs InvertibleNetworks.jl and see what are their differences.

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awesome-normalizing-flows InvertibleNetworks.jl
1 1
1,313 144
- 1.4%
3.6 7.3
about 1 month ago 10 days ago
Python Julia
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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
    1 project | /r/MachineLearning | 22 Jul 2021
    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.

InvertibleNetworks.jl

Posts with mentions or reviews of InvertibleNetworks.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-12.

What are some alternatives?

When comparing awesome-normalizing-flows and InvertibleNetworks.jl you can also consider the following projects:

PyMC - Bayesian Modeling and Probabilistic Programming in Python

Zygote.jl - 21st century AD

nflows - Normalizing flows in PyTorch

JOLI.jl - Julia Operators LIbrary

autoregressive - :kiwi_fruit: Autoregressive Models in PyTorch.

InvertibleNetworks

vbmc - Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB

AlphaZero.jl - A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

Tensorflow-iOS

Flux.jl - Relax! Flux is the ML library that doesn't make you tensor

pyro - Deep universal probabilistic programming with Python and PyTorch