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 …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting
inherently subjects to the following three challenging aspects. First, traffic data are physically …

Deep neural networks have been used in survival prediction by providing high-quality
features. However, few works have noticed the significant role of topological features of …

The hyperspectral image (HSI) classification aims to assign each pixel to a land-cover
category. It is receiving increasing attention from both industry and academia. The main …

Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal
dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring …

Graph convolutional neural networks have aroused more and more attentions on account of
the ability to handle the graph-structured data defined on irregular or non-Euclidean …

Observing the recent progress in Deep Learning, the employment of AI is surging to
accelerate drug discovery and cut R&D costs in the last few years. However, the success of …

Acquiring labeled data has been widely recognized as a major challenge in molecular
property prediction. Since it generally requires a series of specialized biochemical …

Recently, semi-supervised graph learning has attracted growing research interests. Since
the Graph Convolutional Network (GCN) was formulated, some studies argue that shallow …