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GCN with Clustering Coefficients and Attention Module
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020Graph convolutional networks (GCN) exploit graph connectivity through their adjacency matrix. However, the assignment of equal importance to every one-hop neighbor and incognizance of intra-neighbor connectivity restricts its performance. Graph attention networks (GAT) address the problem of treating all neighbors equally by employing a self-attention ...
Rakesh Kumar Yadav +3 more
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TFR-GCN: A GCN Accelerator with Tile-Fusing Strategy
2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2022Shengjun Xu +2 more
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Mal-Bert-GCN: Malware Detection by Combining Bert and GCN
2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2022Zhenquan Ding +5 more
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MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Detection
IEEE Transactions on Biomedical EngineeringResting state fMRI (rsfMRI) is widely used to differentiate Alzheimer's Disease (AD) and identify biomarkers but its obscure features and noises challenge the present models. Brain graph convolution network (GCN) provides a good interpretation but suffers from the inferior performance due to the insufficient feature representation.
Yinghua, Fu, Wenqi, Zhu, Ze, Wang
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MLC-GCN: Multi-Level Connectomes Based GCN for AD Detection
ISMRM Annual MeetingMotivation: Alzheimer's Disease (AD) is characterized by progressive cognitive impairments that are related to alterations in brain functional connectivity (FC). Goal(s): to design a graph convolutional network (GCN) based classifier to differentiate AD from old cognitive normal controls.
Yinghua Fu +4 more
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ASR-GCN: Adaptive spatial information reconstruction GCN for skeleton-based action recognition
Neural NetworksOver the past few years, skeleton-based action recognition has attracted significant focus in the area of computer vision. However, existing methods still face many challenges in feature extraction and dynamic feature learning for complex tasks. This paper proposes an innovative Adaptive Spatial Information Reconstruction Model (ASR-GCN) to address ...
Ying Wu +4 more
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