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Parametric UMAP

4 papers tagged archive 2025-07-28

Introduced by Tim Sainburg et al. in Parametric UMAP embeddings for representation and semi-supervised learning

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Parametric 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.

PaperSource

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Dimensionality Reduction3
Data Visualization2
Embeddings Evaluation1

Usage over time archive 2025-07-28

Papers per year tagged with Parametric UMAP: 2020 to 2023, peak 1 1 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 1 paper 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Dimensionality Reduction

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