33 citations · 100 across the 12 of their papers we have counts for
4 papers · 1 filter
Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, Feng Shi +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming fr…
Alternately Optimized Graph Neural Networks
Haoyu Han, Xiaorui Liu, Haitao Mao +4
Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that ca…
VersaGNN: a Versatile accelerator for Graph neural networks
Feng Shi, Ahren Yiqiao Jin, Song-Chun Zhu
\textit{Graph Neural Network} (GNN) is a promising approach for analyzing graph-structured data that tactfully captures their dependency information via node-level message passing.…
HUGE2: a Highly Untangled Generative-model Engine for Edge-computing
Feng Shi, Ziheng Xu, Tao Yuan +1
As a type of prominent studies in deep learning, generative models have been widely investigated in research recently. Two research branches of the deep learning models, the Genera…