The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

The size of vision models has grown exponentially over the last few years, especially after
the emergence of Vision Transformer. This has motivated the development of parameter …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Recently neural architecture (NAS) search has attracted great interest in academia and
industry. It remains a challenging problem due to the huge search space and computational …

Designing accurate and efficient vision transformers (ViTs) is an important but challenging
task. Supernet-based one-shot neural architecture search (NAS) enables fast architecture …

Abstract Deep Neural Networks have received considerable attention in recent years. As the
complexity of network architecture increases in relation to the task complexity, it becomes …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. Most works of FL employ pre-defined model architectures …

Abstract Neural Architecture Search (NAS) has shown promising performance in the
automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing …