471 citations · 1.1k across the 34 of their papers we have counts for
7 papers · 1 filter
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
Jun Hu, Shengsheng Qian, Quan Fang +1
Graph Neural Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structure-aware features. Existing GNN research designs various prop…
Health Status Prediction with Local-Global Heterogeneous Behavior Graph
Xuan Ma, Xiaoshan Yang, Junyu Gao +1
Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated…
Efficient Graph Deep Learning in TensorFlow with tf_geometric
Jun Hu, Shengsheng Qian, Quan Fang +4
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries f…
MMCGAN: Generative Adversarial Network with Explicit Manifold Prior
Guanhua Zheng, Jitao Sang, Changsheng Xu
Generative Adversarial Network(GAN) provides a good generative framework to produce realistic samples, but suffers from two recognized issues as mode collapse and unstable training…
Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data
Yi Huang, Xiaoshan Yang, Changsheng Xu
Due to potential applications in chronic disease management and personalized healthcare, the EHRs data analysis has attracted much attention of both researchers and practitioners.…
A Generalization Theory based on Independent and Task-Identically Distributed Assumption
Guanhua Zheng, Jitao Sang, Houqiang Li +2
Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which resul…
Understanding Deep Learning Generalization by Maximum Entropy
Guanhua Zheng, Jitao Sang, Changsheng Xu
Deep learning achieves remarkable generalization capability with overwhelming number of model parameters. Theoretical understanding of deep learning generalization receives recent…