Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination

Author:

Li Conglong1,Zhang Minjia2,Andersen David G.1,He Yuxiong2

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA, USA

2. Microsoft AI and Research, Bellevue, WA, USA

Funder

National Science Foundation

Publisher

ACM

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search;Proceedings of the VLDB Endowment;2024-07

2. An Exploration Graph with Continuous Refinement for Efficient Multimedia Retrieval;Proceedings of the 2024 International Conference on Multimedia Retrieval;2024-05-30

3. Vexless: A Serverless Vector Data Management System Using Cloud Functions;Proceedings of the ACM on Management of Data;2024-05-29

4. RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search;Proceedings of the ACM on Management of Data;2024-05-29

5. Applications and Challenges for Large Language Models: From Data Management Perspective;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

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