Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Eldele, Emadeldeen"'
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-world time series often exhibit complex non-uniform distribution with vary
Externí odkaz:
http://arxiv.org/abs/2410.09836
Autor:
Ragab, Mohamed, Gong, Peiliang, Eldele, Emadeldeen, Zhang, Wenyu, Wu, Min, Foo, Chuan-Sheng, Zhang, Daoqiang, Li, Xiaoli, Chen, Zhenghua
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 domain's privacy. While SFDA is prevalent in computer vision, it rema
Externí odkaz:
http://arxiv.org/abs/2406.02635
Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Square Error (MSE) loss often results in over-smooth predictions, making it
Externí odkaz:
http://arxiv.org/abs/2405.18975
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 capturing long-range dependencies, they face limitations in noise sen
Externí odkaz:
http://arxiv.org/abs/2404.08472
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, preserving source domain privacy. Despite its prevalence in visual applications
Externí odkaz:
http://arxiv.org/abs/2307.07542
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-world applications. To address the scarcity of labeled time series dat
Externí odkaz:
http://arxiv.org/abs/2302.06433
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 unlabeled target domain. Despite the prevalence of UDA for visual applicatio
Externí odkaz:
http://arxiv.org/abs/2212.01555
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 possessing a massive amount of labeled data for training, limiting their app
Externí odkaz:
http://arxiv.org/abs/2210.06286
Autor:
Eldele, Emadeldeen, Ragab, Mohamed, Chen, Zhenghua, Wu, Min, Kwoh, Chee-Keong, Li, Xiaoli, Guan, Cuntai
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 shown great improvement in extracting useful representations from unlabel
Externí odkaz:
http://arxiv.org/abs/2208.06616
Autor:
Ragab, Mohamed, Eldele, Emadeldeen, Tan, Wee Ling, Foo, Chuan-Sheng, Chen, Zhenghua, Wu, Min, Kwoh, Chee-Keong, Li, Xiaoli
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 typically developed on image data, and their application to time series dat
Externí odkaz:
http://arxiv.org/abs/2203.08321