73 citations · 316 across the 18 of their papers we have counts for
18 papers
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Yu-Ting Lan, Kan Ren, Yansen Wang +4
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both…
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…
Document-level Neural Machine Translation with Document Embeddings
Shu Jiang, Hai Zhao, Zuchao Li +1
Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense…
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…
Document-level Neural Machine Translation with Associated Memory Network
Shu Jiang, Rui Wang, Zuchao Li +5
Standard neural machine translation (NMT) is on the assumption that the document-level context is independent. Most existing document-level NMT approaches are satisfied with a smat…
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…
Structural Stability of Lexical Semantic Spaces: Nouns in Chinese and French
Sabine Ploux, Rui Wang, ZhengFeng Zhong +3
Many studies in the neurosciences have dealt with the semantic processing of words or categories, but few have looked into the semantic organization of the lexicon thought as a sys…
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
Changde Du, Changying Du, Jinpeng Li +3
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly…
A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
Wei-Long Zheng, Bao-Liang Lu
Objective. Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estima…
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique
Rui Wang, Hai Zhao, Sabine Ploux +3
Most of the existing methods for bilingual word embedding only consider shallow context or simple co-occurrence information. In this paper, we propose a latent bilingual sense unit…
Connecting Phrase based Statistical Machine Translation Adaptation
Rui Wang, Hai Zhao, Bao-Liang Lu +2
Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains with the original corpus can…
Multimodal Emotion Recognition Using Multimodal Deep Learning
Wei Liu, Wei-Long Zheng, Bao-Liang Lu
To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to con…
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of acti…
On the Reducibility of Submodular Functions
Jincheng Mei, Hao Zhang, Bao-Liang Lu
The scalability of submodular optimization methods is critical for their usability in practice. In this paper, we study the reducibility of submodular functions, a property that en…
On Unconstrained Quasi-Submodular Function Optimization
Jincheng Mei, Kang Zhao, Bao-Liang Lu
With the extensive application of submodularity, its generalizations are constantly being proposed. However, most of them are tailored for special problems. In this paper, we focus…