127 citations · 258 across the 13 of their papers we have counts for
5 papers · 1 filter
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
Liang Peng, Nan Wang, Nicha Dvornek +2
Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph,…
Self-paced Principal Component Analysis
Zhao Kang, Hongfei Liu, Jiangxin Li +2
Principal Component Analysis (PCA) has been widely used for dimensionality reduction and feature extraction. Robust PCA (RPCA), under different robust distance metrics, such as l1-…
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
Zhao Kang, Zhiping Lin, Xiaofeng Zhu +1
Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exp…
Neural Network Retraining for Model Serving
Diego Klabjan, Xiaofeng Zhu
We propose incremental (re)training of a neural network model to cope with a continuous flow of new data in inference during model serving. As such, this is a life-long learning pr…
Frosting Weights for Better Continual Training
Xiaofeng Zhu, Feng Liu, Goce Trajcevski +1
Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is t…