Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Space Partitions for Nearest Neighbor Search   Space partitions of $\mathbb{R}^d$ underlie a vast and important class of
fast nearest neighbor search (NNS) algorithms. 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 building space
partitions reducing 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 obtained by Neural LSH consistently outperform partitions found
by quantization-based and tree-based methods as well as classic, data-oblivious
LSH.

0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math