39 citations · 116 across the 8 of their papers we have counts for
15 papers
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
Yuxin Zhang, Jindong Wang, Yiqiang Chen +2
Unsupervised anomaly detection aims to build models to effectively detect unseen anomalies by only training on the normal data. Although previous reconstruction-based methods have…
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
Yuxin Zhang, Yiqiang Chen, Jindong Wang +1
Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can gen…
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
Yiqiang Chen, Wang Lu, Jindong Wang +1
The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data p…
Cross-domain Activity Recognition via Substructural Optimal Transport
Wang Lu, Yiqiang Chen, Jindong Wang +1
It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity reco…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…
FOCUS: Dealing with Label Quality Disparity in Federated Learning
Yiqiang Chen, Xiaodong Yang, Xin Qin +3
Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to s…