AlCorreia/cm-tpm
Code in support of the paper Continuous Mixtures of Tractable Probabilistic Models
This project offers a method for density estimation, which involves understanding the underlying probability distribution of your data. You input a dataset, and it outputs a model that can predict the likelihood of new data points or generate data similar to your input. This is primarily for machine learning researchers or data scientists working with complex datasets.
No commits in the last 6 months.
Use this if you are a machine learning researcher or data scientist experimenting with advanced density estimation techniques on image or benchmark datasets.
Not ideal if you need a plug-and-play solution for general data analysis or if you are not comfortable running Python notebooks for research experiments.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 12, 2024
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