Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

Self-supervised learning (SSL) has been extensively explored in recent years. Particularly,
generative SSL has seen emerging success in natural language processing and other …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Graph contrastive learning (GCL) has emerged as a dominant technique for graph
representation learning which maximizes the mutual information between paired graph …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Molecular property prediction is one of the fastest-growing applications of deep learning with
critical real-world impacts. Although the 3D molecular graph structure is necessary for …