torchflows and PyTorchDiscreteFlows
While both tools implement normalizing flows in PyTorch, `davidnabergoj/torchflows` is a general-purpose, modern library for continuous normalizing flows, making it a competitor to `TrentBrick/PyTorchDiscreteFlows`, which specifically focuses on discrete normalizing flows, catering to a distinct subset of applications.
About torchflows
davidnabergoj/torchflows
Modern normalizing flows in Python. Simple to use and easily extensible.
This library helps machine learning researchers and practitioners train generative models and estimate data density using modern normalizing flows. You provide your dataset, and it outputs a model that can generate new, similar data points or calculate the likelihood of existing ones. It's designed for those working with advanced machine learning models who need flexible tools for generative tasks.
About PyTorchDiscreteFlows
TrentBrick/PyTorchDiscreteFlows
Discrete Normalizing Flows implemented in PyTorch
This is a tool for machine learning researchers and practitioners who are working with discrete data distributions. It helps you model complex, discrete data by transforming simple distributions into more intricate ones. You provide your discrete data, and it outputs a model that can generate similar discrete data or estimate the likelihood of existing data points. It's for those exploring advanced generative models in PyTorch.
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