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RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
Summary: RaBitQ: randomized quantization for ANN in high-dimensional Euclidean space, encoding each D-dim vector into D bits. Key novelty: a sharp theoretical distance-error bound for quantized search, while retaining SIMD/bitwise efficiency and outperforming PQ variants empirically.
(summarized by gpt-5.4-mini on May 24 2026)
Paper ID
h6a01d9752c8694d9
Venue
SIGMOD
Year
2024
Pagerank
0.00013832333
Overall Rank
804 | 94.60%
DOI
10.1145/3654970
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{gao_sigmod24,
title = {{RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search}},
author = {Gao, Jianyang and Long, Cheng},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654970},
url = {https://dl.acm.org/doi/10.1145/3654970},
year = {2024}
}
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Showing 26 of 26 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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45
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1998
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713
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839
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916
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1,170
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1,328
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VLDB
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2022
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1,897
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The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art
2019
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7.9228762e-05
2,969
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3,086
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