pytorch-flows and PyTorchDiscreteFlows
Both libraries are implementations of normalizing flows in PyTorch, making them **competitors** for users seeking algorithms for density estimation, though the second project specifically targets discrete normalizing flows which could imply a niche specialization.
About pytorch-flows
ikostrikov/pytorch-flows
PyTorch implementations of algorithms for density estimation
This project helps researchers and data scientists analyze complex, high-dimensional data to understand the underlying probability distribution. It takes raw numerical datasets, like those found in physics or genetics, and produces a model that can estimate the likelihood of specific data points or generate new, realistic data samples. It's for anyone needing to model complex data distributions without making strong assumptions about their shape.
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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