14 citations · 20 across the 6 of their papers we have counts for
8 papers
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…
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…
Knowledge Distillation with the Reused Teacher Classifier
Defang Chen, Jian-Ping Mei, Hailin Zhang +3
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance. For this purpose, various ap…
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…
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang +5
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…
SamWalker++: recommendation with informative sampling strategy
Can Wang, Jiawei Chen, Sheng Zhou +3
Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the u…