Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

Synthesis planning and reaction outcome prediction are two fundamental problems in
computer-aided organic chemistry for which a variety of data-driven approaches have …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Automatic source code summarization is the task of generating natural language
descriptions for source code. Automatic code summarization is a rapidly expanding research …

Predicting the next interaction of a short-term interaction session is a challenging task in
session-based recommendation. Almost all existing works rely on item transition patterns …

The dominant graph-to-sequence transduction models employ graph neural networks for
graph representation learning, where the structural information is reflected by the receptive …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …