This two-part comprehensive survey is devoted to a computing framework most commonly
known under the names Hyperdimensional Computing and Vector Symbolic Architectures …

This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

Continually learning new classes from fresh data without forgetting previous knowledge of
old classes is a very challenging research problem. Moreover, it is imperative that such …

This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …

Motivated by recent innovations in biologically inspired neuromorphic hardware, this article
presents a novel unsupervised machine learning algorithm named Hyperseed that draws on …

Brain-inspired Hyperdimensional Computing (HDC) is an emerging framework in low-
energy designs for solving classification tasks at the edge. Unlike mainstream neural …

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent
years for its pivotal role in addressing continuously arriving classes. However, it encounters …

Abstract Vector Symbolic Architecture (VSA) is a powerful computing model that is built on a
rich algebra in which all representations—from atomic to composite structures—are high …

In the metric‐based meta‐learning detection model, the distribution of training samples in
the metric space has great influence on the detection performance, and this influence is …

Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic
hardware systems. While there has been substantial progress in SNN research, artificial …