216 citations · 779 across the 38 of their papers we have counts for
20 papers · 1 filter
EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
Edward, Mohamed Ragab, Yuecong Xu +4
Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. Howev…
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele +6
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source…
TSLANet: Rethinking Transformers for Time Series Representation Learning
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +2
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at…
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…
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…
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…
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-…
Contrastive Domain Adaptation for Time-Series via Temporal Mixup
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +3
Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabe…
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has sho…
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan +5
Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typic…
Self-supervised Autoregressive Domain Adaptation for Time Series Data
Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen +3
Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series…
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Yining Ma, Jingwen Li, Zhiguang Cao +4
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP be…
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep s…
Time-Series Representation Learning via Temporal and Contextual Contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representat…
Deep Inertial Odometry with Accurate IMU Preintegration
Rooholla Khorrambakht, Chris Xiaoxuan Lu, Hamed Damirchi +2
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors. They are widely adopted in various auto…
Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction
Mohamed Ragab, Zhenghua Chen, Min Wu +3
Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs.…
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Rui Zhao, Ruqiang Yan, Zhenghua Chen +3
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image…