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- W4313598552 abstract "The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been developed for ANN search that are efficient, accurate, and scalable. However, such techniques typically have a number of parameters that affect the speed-recall tradeoff, and exhibit poor performance when such parameters aren't properly set. Tuning these parameters has traditionally been a manual process, demanding in-depth knowledge of the underlying search algorithm. This is becoming an increasingly unrealistic demand as ANN search grows in popularity. To tackle this obstacle to ANN adoption, this work proposes a constrained optimization-based approach to tuning quantization-based ANN algorithms. Our technique takes just a desired search cost or recall as input, and then generates tunings that, empirically, are very close to the speed-recall Pareto frontier and give leading performance on standard benchmarks." @default.
- W4313598552 created "2023-01-06" @default.
- W4313598552 creator A5001564047 @default.
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- W4313598552 date "2023-01-04" @default.
- W4313598552 modified "2023-10-14" @default.
- W4313598552 title "Automating Nearest Neighbor Search Configuration with Constrained Optimization" @default.
- W4313598552 doi "https://doi.org/10.48550/arxiv.2301.01702" @default.
- W4313598552 hasPublicationYear "2023" @default.
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