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.
A. Hotho, R. J�schke, C. Schmitz, and G. Stumme. The Semantic Web: Research and Applications, volume 4011 of LNAI, page 411-426. Heidelberg, Springer, (June 2006)
Y. Yanbe, A. Jatowt, S. Nakamura, and K. Tanaka. JCDL '07: Proceedings of the 2007 conference on Digital libraries, page 107--116. New York, NY, USA, ACM Press, (2007)
R. Fagin, R. Kumar, and D. Sivakumar. SODA '03: Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms, page 28--36. Philadelphia, PA, USA, Society for Industrial and Applied Mathematics, (2003)
E. Agichtein, and Z. Zheng. KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page 902--908. New York, NY, USA, ACM Press, (2006)