The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

Through the success of artificial neural networks (ANNs) in different domains, intense
research has been recently centered on changing the networks architecture to optimize the …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Vision transformers (ViTs) have recently received explosive popularity, but their enormous
model sizes and training costs remain daunting. Conventional post-training pruning often …

Through the success of deep learning in various domains, artificial neural networks are
currently among the most used artificial intelligence methods. Taking inspiration from the …

Large Language Models (LLMs), renowned for their remarkable performance across diverse
domains, present a challenge when it comes to practical deployment due to their colossal …

In this paper, we introduce a new perspective on training deep neural networks capable of
state-of-the-art performance without the need for the expensive over-parameterization by …

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …

The receptive field (RF), which determines the region of time series to be “seen” and used, is
critical to improve the performance for time series classification (TSC). However, the …

The continual learning (CL) paradigm aims to enable neural networks to learn tasks
continually in a sequential fashion. The fundamental challenge in this learning paradigm is …