72 citations · 220 across the 23 of their papers we have counts for
11 papers · 1 filter
Local Contrastive Feature learning for Tabular Data
Zhabiz Gharibshah, Xingquan Zhu
Contrastive self-supervised learning has been successfully used in many domains, such as images, texts, graphs, etc., to learn features without requiring label information. In this…
Deep Forest with Hashing Screening and Window Screening
Pengfei Ma, Youxi Wu, Yan Li +4
As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vecto…
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…
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
Zhabiz Gharibshah, Xingquan Zhu
In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entiti…
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…
Transfer Learning across Networks for Collective Classification
Meng Fang, Jie Yin, Xingquan Zhu
This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning re…