8 papers · 1 filter
Uncertainty-Aware Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
Graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph…
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…
Online Adversarial Knowledge Distillation for Graph Neural Networks
Can Wang, Zhe Wang, Defang Chen +3
Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both…
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
Weiqin Yang, Jiawei Chen, Xin Xin +5
Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant lim…
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…
A Note on Knowledge Distillation Loss Function for Object Classification
Defang Chen
This research note provides a quick introduction to the knowledge distillation loss function used in object classification. In particular, we discuss its connection to a previously…