216 citations · 666 across the 28 of their papers we have counts for
8 papers · 1 filter
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…
SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance
Li Lyna Zhang, Youkow Homma, Yujing Wang +5
Ad relevance modeling plays a critical role in online advertising systems including Microsoft Bing. To leverage powerful transformers like BERT in this low-latency setting, many ex…
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…
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Haozhi Cao +4
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to impr…
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
Zhenghua Chen, Min Wu, Alvin Chan +2
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for…