46 citations · 70 across the 12 of their papers we have counts for
9 papers · 1 filter
BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
Guanghui Zhu, Xin Fang, Feng Cheng +4
Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning tas…
PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs
Tongzhou Yu, Zhuhao Zhang, Guanghui Zhu +3
Parameter Efficient Fine-Tuning (PEFT) methods have emerged as effective and promising approaches for fine-tuning pre-trained language models. Compared with Full parameter Fine-Tun…
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
Guanghui Zhu, Zipeng Ji, Jingyan Chen +3
GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such a…
Simple and Efficient Partial Graph Adversarial Attack: A New Perspective
Guanghui Zhu, Mengyu Chen, Chunfeng Yuan +1
As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack meth…
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
Guanghui Zhu, Zhennan Zhu, Hongyang Chen +2
Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent…
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network
Guanghui Zhu, Zhennan Zhu, Wenjie Wang +3
Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural netw…