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On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations

Summary: AQNN: aggregate statistics over the learned-representation neighborhood of a query object. Key idea is SPRinT, mixing high-quality but expensive embeddings with cheap ones via sampling + precision/recall-targeted NN selection, with error/sample-size bounds. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID
7498
Venue
SIGMOD
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,187 | 29.14%
DOI
10.1145/3786672

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