This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory
approximate nearest neighbor algorithms. It provides a standard interface for measuring the …

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer
science with numerous applications in (eg,) machine learning and data mining. Recent …

Recent advances by practitioners in the deep learning community have breathed new life
into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in …

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching
(MT) algorithms for online personalized education on home devices. This direction becomes …

Locality-Sensitive Hashing (LSH) is one of the most popular methods for c-Approximate
Nearest Neighbor Search (c-ANNS) in high-dimensional spaces. In this paper, we propose a …

We study the problem of $\text {\emph {vector set search}} $ with $\text {\emph {vector set
queries}} $. This task is analogous to traditional near-neighbor search, with the exception …

Entity resolution identifies and removes duplicate entities in large, noisy databases and has
grown in both usage and new developments as a result of increased data availability …

We present a new algorithm for the approximate near neighbor problem that combines
classical ideas from group testing with locality-sensitive hashing (LSH). We reduce the near …

Nearest neighbors search is a core operation found in several online multimedia services.
These services have to handle very large databases, while, at the same time, they must …

Constructing a similarity graph from a set $ X $ of data points in $\mathbb {R}^ d $ is the first
step of many modern clustering algorithms. However, typical constructions of a similarity …