7 citations · 14 across the 8 of their papers we have counts for
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cs.LG2023★ 7 cited
Relating tSNE and UMAP to Classical Dimensionality Reduction
Andrew Draganov, Simon Dohn
It has become standard to use gradient-based dimensionality reduction (DR) methods like tSNE and UMAP when explaining what AI models have learned. This makes sense: these methods a…
cs.LG2023
ActUp: Analyzing and Consolidating tSNE and UMAP
Andrew Draganov, Jakob Rødsgaard Jørgensen, Katrine Scheel Nellemann +4
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has be…