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.
586 stars. No commits in the last 6 months.
Use this if you need to accurately model the probability distribution of high-dimensional, continuous data like scientific measurements or financial time series.
Not ideal if your data is primarily categorical, images, or if you need to perform tasks like classification or regression rather than density estimation.
Stars
586
Forks
75
Language
Python
License
MIT
Category
Last pushed
May 13, 2021
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