14 citations · 27 across the 8 of their papers we have counts for
8 papers
Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution
Yingqi Feng, Yufei Tang, Min Shi +1
Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups or consists of multiple obje…
GraSSNet: Graph Soft Sensing Neural Networks
Yu Huang, Chao Zhang, Jaswanth Yella +5
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obta…
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
Yu Huang, James Li, Min Shi +5
Ocean current, fluid mechanics, and many other spatio-temporal physical dynamical systems are essential components of the universe. One key characteristic of such systems is that c…
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
Yu Huang, Yufei Tang, Xingquan Zhu +4
Spatio-temporal forecasting is of great importance in a wide range of dynamical systems applications from atmospheric science, to recent COVID-19 spread modeling. These application…
Evolutionary Architecture Search for Graph Neural Networks
Min Shi, David A. Wilson, Xingquan Zhu +4
Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen…
Topology and Content Co-Alignment Graph Convolutional Learning
Min Shi, Yufei Tang, Xingquan Zhu
In traditional Graph Neural Networks (GNN), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learni…
Multi-Label Graph Convolutional Network Representation Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with…
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have be…