216 citations · 540 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning
Armi Tiihonen, Sarah J. Cox-Vazquez, Qiaohao Liang +12
New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug, takes decades. Although progress…
Robust Domain-Free Domain Generalization with Class-aware Alignment
Wenyu Zhang, Mohamed Ragab, Ramon Sagarna
While deep neural networks demonstrate state-of-the-art performance on a variety of learning tasks, their performance relies on the assumption that train and test distributions are…