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• W2913031009 abstract "Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space $mathbb{R}^d$. Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification. We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH). On several standard benchmarks for NNS, our experiments show that the partitions found by Neural LSH consistently outperform partitions found by quantization- and tree-based methods." @default.
• W2913031009 created "2019-02-21" @default.
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• W2913031009 date "2019-01-24" @default.
• W2913031009 modified "2023-09-27" @default.
• W2913031009 title "Learning Sublinear-Time Indexing for Nearest Neighbor Search." @default.
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• W2913031009 hasPublicationYear "2019" @default.
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