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pro vyhledávání: '"Zeng, Qiuhao"'
Autor:
Zeng, Qiuhao, Wang, Wei, Zhou, Fan, Xu, Gezheng, Pu, Ruizhi, Shui, Changjian, Gagne, Christian, Yang, Shichun, Wang, Boyu, Ling, Charles X.
In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynami
Externí odkaz:
http://arxiv.org/abs/2402.07834
Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indices. For example, a self-drivi
Externí odkaz:
http://arxiv.org/abs/2301.07845
Unsupervised domain adaptation (UDA) enables knowledge transfer from the labelled source domain to the unlabeled target domain by reducing the cross-domain discrepancy. However, most of the studies were based on direct adaptation from the source doma
Externí odkaz:
http://arxiv.org/abs/2202.10000
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a
Externí odkaz:
http://arxiv.org/abs/2105.02786
Publikováno v:
IEEE Transactions on Affective Computing 2022
The high temporal resolution and the asymmetric spatial activations are essential attributes of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the temporal dynamics and spatial asymmetry of EEG towards accurate and g
Externí odkaz:
http://arxiv.org/abs/2104.02935
Autor:
Ding, Yi, Robinson, Neethu, Zeng, Qiuhao, Chen, Duo, Wai, Aung Aung Phyo, Lee, Tih-Shih, Guan, Cuntai
In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel
Externí odkaz:
http://arxiv.org/abs/2004.02965
Autor:
Zhou, Fan, Chen, Yuyi, Wen, Jun, Zeng, Qiuhao, Shui, Changjian, Ling, Charles X., Yang, Shichun, Wang, Boyu
Publikováno v:
In Neural Networks May 2023 162:34-45
Akademický článek
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Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; 2024, Vol. 35 Issue: 7 p9773-9786, 14p
Akademický článek
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