The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

In recent years, we have witnessed the developments that deep learning has brought to
machine learning. It has solved many problems in the areas of computer vision, speech …

Deep learning's success has been widely recognized in a variety of machine learning tasks,
including image classification, audio recognition, and natural language processing. As an …

Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …

This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Graph Convolutional Network (GCN) which models the potential relationship
between non-Euclidean spatial data has attracted researchers' attention in deep learning in …

In recent years, geometric deep learning methods have been proposed, which are called
Graph Convolutional Neural Networks (GCNNs). GCNNs not only can extract effective …