This paper addresses the clustering of data in the hyperdimensional computing (HDC)
domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has …

Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …

Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …

As current shotgun proteomics experiments can produce gigabytes of mass spectrometry
data per hour, processing these massive data volumes has become progressively more …

Hyperdimensional computing (HDC) has emerged as an alternative lightweight learning
solution to deep neural networks. A key characteristic of HDC is the great extent of …

Motivation Driven by technological advances, the throughput and cost of mass spectrometry
(MS) proteomics experiments have improved by orders of magnitude in recent decades …

Hyperdimensional (HD) computing is a form of brain-inspired computing which can be
applied to numerous classification problems. In past research, it has been shown that …

Mass spectrometry, commonly used for protein identification, generates a massive number
of spectra that need to be matched against a large database. In reality, most of them remain …

This paper addresses clustering of clinical response of subjects with major depressive
disorder (MDD) after they are treated with transcranial magnetic stimulation (TMS) …

Hyperdimensional computing (HDC) is a computing paradigm that draws inspiration from
human memory models. It represents data in the form of high-dimensional vectors. Recently …