216 citations · 518 across the 16 of their papers we have counts for
18 papers
A Unified Shape-Aware Foundation Model for Time Series Classification
Zhen Liu, Yucheng Wang, Boyuan Li +4
Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundatio…
Learning Soft Sparse Shapes for Efficient Time-Series Classification
Zhen Liu, Yicheng Luo, Boyuan Li +3
Shapelets are discriminative subsequences (or shapes) with high interpretability in time series classification. Due to the time-intensive nature of shapelet discovery, existing sha…
Time Series Domain Adaptation via Latent Invariant Causal Mechanism
Ruichu Cai, Junxian Huang, Zhenhui Yang +4
Time series domain adaptation aims to transfer the complex temporal dependence from the labeled source domain to the unlabeled target domain. Recent advances leverage the stable ca…
Retrieving Filter Spectra in CNN for Explainable Sleep Stage Classification
Stephan Goerttler, Yucheng Wang, Fei He +1
Despite significant advances in deep learning-based sleep stage classification, the clinical adoption of automatic classification models remains slow. One key challenge is the lack…
MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification
Stephan Goerttler, Yucheng Wang, Emadeldeen Eldele +2
Recent advancements in machine learning-based signal analysis, coupled with open data initiatives, have fuelled efforts in automatic sleep stage classification. Despite the prolife…
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
Yanru Sun, Zongxia Xie, Emadeldeen Eldele +3
Time series forecasting, which aims to predict future values based on historical data, has garnered significant attention due to its broad range of applications. However, real-worl…
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…
Hierarchical Classification Auxiliary Network for Time Series Forecasting
Yanru Sun, Zongxia Xie, Dongyue Chen +2
Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Squar…
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…
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 Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +3
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to posses…
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…
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…