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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
0.00013822222
Overall Rank
802 | 94.63%
DOI
10.1145/3654970
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(CC BY-NC-SA 4.0)
Incoming Non-self Citations Over Time
BibTeX Citation
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@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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