Measuring similarity of neural networks to understand and improve their behavior has
become an issue of great importance and research interest. In this survey, we provide a …

Despite the popularization of deep neural networks (DNNs) in many fields, it is still
challenging to deploy state-of-the-art models to resource-constrained devices due to high …

Sparse neural networks can achieve performance comparable to fully connected networks
but need less energy and memory, showing great promise for deploying artificial intelligence …

Understanding the enigmatic black-box representations within Deep Neural Networks
(DNNs) is an essential problem in the community of deep learning. An initial step towards …

With the rapid development of industrial automation, automatic reading of pointer meters has
become a trend of data monitoring and efficient measurement in the industrial field. In the …

The deployment of Deep Neural Network (DNN)-based networks on resource-constrained
devices remains a significant challenge due to their high computational and parameter …

Although deep neural networks have developed at a high level, the large computational
requirement limits the deployment in end devices. To this end, a variety of model …

With the successful application of deep learning in communications systems, deep neural
networks are becoming the preferred method for signal classification. Although these …

Within one decade, Deep Learning overtook the dominating solution methods of countless
problems of artificial intelligence.``Deep''refers to the deep architectures with operations in …

Recently, the field of machine learning has undergone a transition from model-centric to
data-centric. The advancements in diverse learning tasks have been propelled by the …