In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …

The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …

The prevalence of graph-based data has spurred the rapid development of graph neural
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …

Accurate prediction of groundwater level (GWL) over a period of time is of great importance
for groundwater resources management. Machine learning techniques due to their great …

Abstract Semantic enrichment of Building Information Modeling (BIM) models supplements
models with the implicit semantics for further applications. In this paper, we use the room …

Abstract Graph Convolutional Networks (GCNs) have shown promising results in modeling
graph-structured data. However, they have difficulty with processing digraphs because of …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

This paper analyses a set of simple adaptations that transform standard message-passing
Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks …

Despite the impressive performance obtained by recent single-image hand modeling
techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This …

Query performance prediction is vital to many database tasks (eg, database monitoring and
query scheduling). Existing methods focus on predicting the performance for a single query …