40 citations · 51 across the 3 of their papers we have counts for
4 papers · 1 filter
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Peng Xu, Lin Zhang, Xuanzhou Liu +4
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures. However, these me…
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
Xuanzhou Liu, Lin Zhang, Jiaqi Sun +2
Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature. Existing studies usually tackle it by…
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
Xuan Liu, Xiaoguang Wang, Stan Matwin
Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performanc…
Interpretable Deep Convolutional Neural Networks via Meta-learning
Xuan Liu, Xiaoguang Wang, Stan Matwin
Model interpretability is a requirement in many applications in which crucial decisions are made by users relying on a model's outputs. The recent movement for "algorithmic fairnes…