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

Self-powered neurostimulation via biomaterials and bioelectronics innovation has emerged
as a compelling approach to explore, repair, and modulate neural systems. This review …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as
textual glosses. Recent studies show that insufficient training caused by the lack of large …

Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs)
for molecules. Typically, atom types as node attributes are randomly masked and GNNs are …

Deep learning has achieved remarkable success in learning representations for molecules,
which is crucial for various biochemical applications, ranging from property prediction to …

The utilization of word embeddings—powerful models computed through Neural Network
architectures that encode words as vectors—has witnessed rapid growth across various …

Based on the foundation of Large Language Models (LLMs), Multilingual Large Language
Models (MLLMs) have been developed to address the challenges of multilingual natural …

Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which
has recently gained considerable attention thanks to advances in deep neural networks …

Pretrained protein structure models without labels are crucial foundations for the majority of
protein downstream applications. The conventional structure pretraining methods follow the …