K-Nearest-Rank Scores: A Tool for Local Structure Analysis in Embeddings
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Erdogan
You will get a stand-alone document that you can download, revisit anytime, and learn from at your own pace. To make it even more engaging, each guide also comes with a podcast version. Now you can also listen on the go, whether you’re commuting, exercising, or just taking a break from screens.
K-Nearest-Rank Scores: A Tool for Local Structure Analysis in Embeddings.
When using a low-dimensional projection, we should always be aware that it is merely a projection of a higher and more complex dimensional space. To truly trust these projections, we need to assess how well they preserve the original sample distribution.
You will get a stand-alone document together with a podcast
Stand-alone document
Podcast
Size
25.9 MB
Duration
14 minutes
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