timsainb/ParametricUMAP_paper
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
This project provides the original code for Parametric UMAP, a method to learn efficient data representations. It takes in high-dimensional datasets and outputs a lower-dimensional embedding that captures the structure and relationships within the data. Data scientists, machine learning engineers, and researchers can use this to improve semi-supervised learning tasks.
152 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer who needs a highly customizable implementation of Parametric UMAP to integrate into your deep neural network models or reproduce specific research results.
Not ideal if you're looking for the standard, user-friendly implementation of Parametric UMAP; in that case, you should use the main UMAP library (v0.5+).
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Jupyter Notebook
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MIT
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Last pushed
Feb 08, 2021
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