https://github.com/awehenkel/normalizing-flows
This repository offers an implementation of some common architectures for normalizing flows.
https://github.com/awehenkel/normalizing-flows
density-estimation neural-network normalizing-flows
Last synced: 4 days ago
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This repository offers an implementation of some common architectures for normalizing flows.
- Host: GitHub
- URL: https://github.com/awehenkel/normalizing-flows
- Owner: AWehenkel
- License: bsd-3-clause
- Created: 2020-11-18T13:16:55.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-17T21:40:37.000Z (about 4 years ago)
- Last Synced: 2025-03-31T23:51:26.862Z (about 2 months ago)
- Topics: density-estimation, neural-network, normalizing-flows
- Language: Jupyter Notebook
- Homepage:
- Size: 979 KB
- Stars: 13
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Normalizing-Flows
This repository offers an implementation of some of the most common architectures for normalizing flows. If you are here you may be interested by this nice repository https://github.com/bayesiains/nflows which contains many flow components implementation as well!
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## Installation:
`pip install git+https://github.com/AWehenkel/Normalizing-Flows/`
## Dependencies
- torch (> 1.5)
- numpy
- umnn (pip install umnn)
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Still in progress, you can check the models folder for basic implementation of affine and monotonic transformations
and coupling or autorgressive conditioners.
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## Short tutorials:
### 2D toy problems
This one shows how to build simple autoregressive feed-forward normalizing flows.
### Flow for images
To come...
### Conditional normalizing flows
To come...
### Graphical normalizing flows
To come...