The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance
that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal …

Many modern methods for prediction leverage nearest neighbor search to find past training
examples most similar to a test example, an idea that dates back in text to at least the 11th …

The nearest neighbor problem is defined as follows: Given a set P of n points in some metric
space (X, D), build a data structure that, given any point q, returns a point in P that is closest …

We consider the problem of clustering a collection of elements based on pairwise judgments
of similarity and dissimilarity. Bansal et al.(in: Proceedings of 43rd FOCS, 2002, pp. 238 …

Machine learning, graph analytics and sparse linear algebra-based applications are
dominated by irregular memory accesses resulting from following edges in a graph or non …

As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes
ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS …

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally
expensive due to the curse of dimensionality. As a well-known solution to approximate NN …

Over the past, deep neural networks have proved to be an essential element for developing
intelligent solutions. They have achieved remarkable performances at a cost of deeper …