3 papers
cs.LG2024
Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power
Zhe Wang, Tianjian Zhao, Zhen Zhang +5
Dynamic Graph Neural Networks (DyGNNs) have garnered increasing research attention for learning representations on evolving graphs. Despite their effectiveness, the limited express…
cs.LG2024
Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding
Zhe Wang, Sheng Zhou, Jiawei Chen +5
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling compl…
cs.CV2020
Cross-Layer Distillation with Semantic Calibration
Defang Chen, Jian-Ping Mei, Yuan Zhang +3
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model. Recently, feature-map based variants expl…