11 citations · 19 across the 5 of their papers we have counts for
8 papers
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks
Hao Tang, Zhiao Huang, Jiayuan Gu +2
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems. Tak…
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models
Yun Luo, Li-Zhen Zhu, Zi-Yu Wan +1
The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algo…
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition
Xun Wu, Wei-Long Zheng, Bao-Liang Lu
Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity…
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
Wei Liu, Jie-Lin Qiu, Wei-Long Zheng +1
Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical c…
Judging Chemical Reaction Practicality From Positive Sample only Learning
Shu Jiang, Zhuosheng Zhang, Hai Zhao +4
Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automat…
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
Changde Du, Changying Du, Hao Wang +4
There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to m…