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 …

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …

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 …

Graph mining is one of the most important areas in data mining. However, scalable solutions
for graph mining are still lacking as existing studies focus on sequential algorithms. While …

Deep hashing methods have been proved to be effective for the large-scale medical image
search assisting reference-based diagnosis for clinicians. However, when the salient region …

The industry practice has been moving to embedding based retrieval (EBR). For example, in
many applications, the embedding vectors are trained by some form of two-tower models …

Network embedding has been widely used in social recommendation and network analysis,
such as recommendation systems and anomaly detection with graphs. However, most of …

Locality Sensitive Hashing (LSH) is widely adopted to index similar data in high-dimensional
space for approximate nearest neighbor search. With the rapid increase of datasets, recent …

Similarity query is the family of queries based on some similarity metrics. Unlike the
traditional database queries which are mostly based on value equality, similarity queries aim …

As an effective solution to the approximate nearest neighbors (ANN) search problem,
learning to hash (L2H) is able to learn similarity-preserving hash functions tailored for a …