216 citations · 779 across the 38 of their papers we have counts for
12 papers · 1 filter
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Yucheng Wang, Yuecong Xu, Jianfei Yang +4
Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between a labeled source domain and an unlabeled…
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
Hongxiang Gao, Xiangyao Wang, Zhenghua Chen +5
Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challen…
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Yucheng Wang, Yuecong Xu, Jianfei Yang +4
Multivariate Time-Series (MTS) data is crucial in various application fields. With its sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits Spati…
Graph-Aware Contrasting for Multivariate Time-Series Classification
Yucheng Wang, Yuecong Xu, Jianfei Yang +4
Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views o…
Unlimited Knowledge Distillation for Action Recognition in the Dark
Ruibing Jin, Guosheng Lin, Min Wu +4
Dark videos often lose essential information, which causes the knowledge learned by networks is not enough to accurately recognize actions. Existing knowledge assembling methods re…
Shuffled Differentially Private Federated Learning for Time Series Data Analytics
Chenxi Huang, Chaoyang Jiang, Zhenghua Chen
Trustworthy federated learning aims to achieve optimal performance while ensuring clients' privacy. Existing privacy-preserving federated learning approaches are mostly tailored fo…
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
Mohamed Ragab, Emadeldeen Eldele, Min Wu +3
Source-free domain adaptation (SFDA) aims to adapt a pretrained model from a labeled source domain to an unlabeled target domain without access to the source domain data, preservin…
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
Qing Xu, Min Wu, Xiaoli Li +2
For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource-limited environments (e.g., smartphone…
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
Hongxiang Gao, Xingyao Wang, Zhenghua Chen +3
Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devi…
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Yunjiao Zhou +3
For video models to be transferred and applied seamlessly across video tasks in varied environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to improve the…
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation
Xiyu Wang, Yuecong Xu, Jianfei Yang +2
Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains continuously without…
Label-efficient Time Series Representation Learning: A Review
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +3
Label-efficient time series representation learning, which aims to learn effective representations with limited labeled data, is crucial for deploying deep learning models in real-…