Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …

A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

Where am I? This is one of the most critical questions that any intelligent system should
answer to decide whether it navigates to a previously visited area. This problem has long …

For many computer vision and machine learning problems, large training sets are key for
good performance. However, the most computationally expensive part of many computer …

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved
great success for fast high-recall search, but are extremely expensive when handling very …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

Current state-of-the-art approximate nearest neighbor search (ANNS) algorithms generate
indices that must be stored in main memory for fast high-recall search. This makes them …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

Many graph-based semi-supervised learning methods for large datasets have been
proposed to cope with the rapidly increasing size of data, such as Anchor Graph …