zuko and normalizing-flows
The tools are competitors, as `probabilists/zuko` is a well-established and actively used library for implementing normalizing flows in PyTorch, while `abdulfatir/normalizing-flows` appears to be a less maintained or pedagogical repository aiming to explain the same concepts, suggesting that a user would choose one over the other based on their need for a production-ready library versus a conceptual understanding.
About zuko
probabilists/zuko
Normalizing flows in PyTorch
This project helps machine learning engineers and researchers build advanced probabilistic models. It takes in structured data and outputs flexible, high-dimensional probability distributions that can be easily trained and sampled. It is ideal for those working on complex density estimation or generative modeling tasks.
About normalizing-flows
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.
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