janosh/awesome-normalizing-flows
A curated list of resources for understanding and applying normalizing flows, a generative ML technique for constructing expressive probability distributions.
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This repository compiles awesome resources covering normalizing flows, a deep learning technique for density estimation and generative modeling. It serves as a reference collection for researchers and practitioners, organizing publications, implementations, applications, and educational videos. Normalizing flows work by chaining trainable bijective transformations to transform simple base distributions into complex ones, enabling tasks like variational inference and Bayesian neural network posterior approximation.
Frequently asked
- What is janosh/awesome-normalizing-flows?
- A curated list of resources for understanding and applying normalizing flows, a generative ML technique for constructing expressive probability distributions.
- Is awesome-normalizing-flows open source?
- Yes — janosh/awesome-normalizing-flows is open source, released under the MIT license.
- What language is awesome-normalizing-flows written in?
- janosh/awesome-normalizing-flows is primarily written in Python.
- How popular is awesome-normalizing-flows?
- janosh/awesome-normalizing-flows has 1.6k stars on GitHub.
- Where can I find awesome-normalizing-flows?
- janosh/awesome-normalizing-flows is on GitHub at https://github.com/janosh/awesome-normalizing-flows.