127 citations · 258 across the 13 of their papers we have counts for
13 papers
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,…
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
Jie Xu, Yazhou Ren, Huayi Tang +4
Multi-view clustering, a long-standing and important research problem, focuses on mining complementary information from diverse views. However, existing works often fuse multiple v…
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-…
Self-paced Resistance Learning against Overfitting on Noisy Labels
Xiaoshuang Shi, Zhenhua Guo, Kang Li +2
Noisy labels composed of correct and corrupted ones are pervasive in practice. They might significantly deteriorate the performance of convolutional neural networks (CNNs), because…
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…
Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
Xiaofeng Zhu, Bin Song, Feng Shi +9
With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might conve…
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…
Listwise Learning to Rank by Exploring Unique Ratings
Xiaofeng Zhu, Diego Klabjan
In this paper, we propose new listwise learning-to-rank models that mitigate the shortcomings of existing ones. Existing listwise learning-to-rank models are generally derived from…
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…
High dynamic range image forensics using cnn
Yongqing Huo, Xiaofeng Zhu
High dynamic range (HDR) imaging has recently drawn much attention in multimedia community. In this paper, we proposed a HDR image forensics method based on convolutional neural ne…
Identification of multi-scale hierarchical brain functional networks using deep matrix factorization
Hongming Li, Xiaofeng Zhu, Yong Fan
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organizatio…
Semantic Document Distance Measures and Unsupervised Document Revision Detection
Xiaofeng Zhu, Diego Klabjan, Patrick Bless
In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new doc…
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
Xiaofeng Zhu, Diego Klabjan, Patrick Bless
In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a lar…