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
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Dimensionality Reduction | 3 | 50.00% |
| Data Visualization | 2 | 33.33% |
| Embeddings Evaluation | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
