Document - Vec2Doc: transforming dense vectors into sparse representations for efficient information retrieval

2023

Conference article Open Access

Vec2Doc: transforming dense vectors into sparse representations for efficient information retrieval

Carrara F., Gennaro C., Vadicamo L., Amato G.

Inverted index Approximate search High-dimensional indexing Very large databases Surrogate text representation

Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval

Source: SISAP 2023 - 16th International Conference on Similarity Search and Applications, pp. 215–222, A Coruña, Spain, 9-11/10/2023

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@inproceedings{oai:it.cnr:prodotti:488211,
	title = {Vec2Doc: transforming dense vectors into sparse representations for efficient information retrieval},
	author = {Carrara F. and Gennaro C. and Vadicamo L. and Amato G.},
	doi = {10.1007/978-3-031-46994-7_18},
	booktitle = {SISAP 2023 - 16th International Conference on Similarity Search and Applications, pp. 215–222, A Coruña, Spain, 9-11/10/2023},
	year = {2023}
}