Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.04035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917353878454272 |
|---|---|
| author | Xiao, Han |
| author_facet | Xiao, Han |
| contents | mlx-vis implements eight dimensionality reduction methods -- UMAP, t-SNE, PaCMAP, LocalMAP, TriMap, DREAMS, CNE, MMAE -- and NNDescent k-NN graph construction entirely in MLX for Apple Silicon Metal GPU. A built-in GPU renderer produces scatter plots and smooth animations via hardware H.264 encoding. On Fashion-MNIST (70K points, M3 Ultra), seven of eight methods embed in 2.0-4.7s and render 800-frame animations in 1.4s. The library depends only on MLX and NumPy and is available at https://github.com/hanxiao/mlx-vis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon Xiao, Han Machine Learning mlx-vis implements eight dimensionality reduction methods -- UMAP, t-SNE, PaCMAP, LocalMAP, TriMap, DREAMS, CNE, MMAE -- and NNDescent k-NN graph construction entirely in MLX for Apple Silicon Metal GPU. A built-in GPU renderer produces scatter plots and smooth animations via hardware H.264 encoding. On Fashion-MNIST (70K points, M3 Ultra), seven of eight methods embed in 2.0-4.7s and render 800-frame animations in 1.4s. The library depends only on MLX and NumPy and is available at https://github.com/hanxiao/mlx-vis. |
| title | mlx-vis: GPU-Accelerated Dimensionality Reduction and Visualization on Apple Silicon |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.04035 |