A very common workflow is to index some data based on its embeddings and then given a new query embedding retrieve the most similar examples with k-Nearest Neighbor search. For example, you can imagine embedding a large collection of papers by their abstracts and then given a new paper of interest retrieve the most similar papers to it.
TLDR in my experience it ~always works better to use an SVM instead of kNN, if you can afford the slight computational hit
Democracy was dealt a major blow in 2020. Almost 70% of countries covered by The Economist Intelligence Unit’s Democracy Index recorded a decline in their overall score, as country after country locked down to protect lives from a novel coronavirus. Find out more in our recent report.
Der Index vergleicht Entwicklerzufriedenheit global und ist in der Regionalausgabe für Deutschland erschienen, er differenziert auch nach Alter und Geschlecht.
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