Neighbor-Sensitive Hashing
Summary: Neighbor-Sensitive Hashing reframes kNN hashing by increasing distance between similar items in hash space. Theoretical analysis and a practical algorithm show improved efficiency and accuracy on benchmarks versus state-of-the-art methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yongjoo Park
- 2. Michael Cafarella
- 3. Barzan Mozafari
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,204 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00013319541 |
| 1,364 | Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination | 2020 | SIGMOD | 0.00012370117 |
| 2,523 | ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data | 2024 | SIGMOD | 8.604576e-05 |
| 2,588 | Database Learning: Toward a Database that Becomes Smarter Every Time | 2017 | SIGMOD | 8.4909562e-05 |
| 5,806 | BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees | 2019 | SIGMOD | 5.3200643e-05 |
| 6,796 | InferDB: In-Database Machine Learning Inference Using Indexes | 2024 | VLDB | 4.9241624e-05 |
| 11,194 | A Step Toward Deep Online Aggregation | 2023 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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