72 citations · 220 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Predictive Modeling of Hospital Readmission: Challenges and Solutions
Shuwen Wang, Xingquan Zhu
Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days…
Learning Graph Neural Networks with Positive and Unlabeled Nodes
Man Wu, Shirui Pan, Lan Du +1
Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdep…
User Response Prediction in Online Advertising
Zhabiz Gharibshah, Xingquan Zhu
Online advertising, as the vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The…