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)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 332 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00027173479 |
| 416 | Approximate Query Processing Using Wavelets | 2000 | VLDB | 0.00023773968 |
| 596 | Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search | 2016 | VLDB | 0.00019455943 |
| 1,013 | HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces | 2018 | VLDB | 0.00014632051 |
| 1,347 | Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination | 2020 | SIGMOD | 0.00012463441 |
| 3,553 | Approximate Selection with Guarantees using Proxies | 2020 | VLDB | 6.9763548e-05 |
| 6,184 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.1636368e-05 |
| 9,311 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 4.3535588e-05 |
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