3 citations · 7 across the 5 of their papers we have counts for
5 papers
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
Zhichun Guo, Chunhui Zhang, Yujie Fan +3
Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they lea…
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Chunhui Zhang, Chao Huang, Yijun Tian +5
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs,…
Heterogeneous Graph Masked Autoencoders
Yijun Tian, Kaiwen Dong, Chunhui Zhang +2
Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph da…
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
Chunhui Zhang, Xiaoming Yuan, Qianyun Zhang +3
Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning…
FDNAS: Improving Data Privacy and Model Diversity in AutoML
Chunhui Zhang, Yongyuan Liang, Xiaoming Yuan +1
To prevent the leakage of private information while enabling automated machine intelligence, there is an emerging trend to integrate federated learning and Neural Architecture Sear…