Dimensionality Reduction
Parametric UMAP
Introduced by Sainburg et al. in Parametric UMAP embeddings for representation and semi-supervised learningParametric UMAP is a non-parametric graph-based dimensionality reduction algorithm that extends the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding.
Source: Parametric UMAP embeddings for representation and semi-supervised learning
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
