abdulfatir/normalizing-flows
Understanding normalizing flows
This project helps machine learning researchers and practitioners understand and experiment with normalizing flows, specifically planar and radial flows. It takes in existing complex 2D data distributions and outputs transformed samples that can be used for variational inference. This is ideal for those working on probabilistic modeling and generative tasks.
132 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or student exploring generative models and want to understand how normalizing flows can transform complex probability distributions.
Not ideal if you need a production-ready, robust implementation for Variational Autoencoders (VAEs) with planar flows, as the VAE+PF implementation in this repository has known bugs.
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132
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Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 02, 2019
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