Graph neural networks (GNN) has been demonstrated to be effective in classifying graph structures. To further improve the graph representation learning ability, hierarchical GNN has been explored. It leverages the differentiable pooling to cluster nodes into fixed groups, and generates a coarse-grained...
SCC-MPGCN: Self-Attention Coherence Clustering Based on Multi-Pooling Graph Convolutional Network for EEG Emotion Recognition The purpose of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity of aspects in a given sentence. Most historical works on senti... H Zhao,J Liu...
Pooling networkMulti-channel fusionConvolutional Neural networkWe combine SNet network based on gating mechanism with poolnet network to solve the problem of salient object detection. The network construction of this paper is based on FPN, which is a classic U-net backbone network. Inspired by ...
Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing de
Heterogeneous graphical neural network Graph theory is widely used in supply chains to analyze complex networks (Guerrero-Lorente et al., 2020). Suppose a graph network G=(V,E,A), where V is the set of n nodes, E is the set of edges between nodes, and A={zi∣vi∈V} characterizes ...
AvgPools(F) denotes the global average pooling operation over the spatial dimension of the input feature map. f2D1×1, f2D3×3, and f2D5×5 denote the 2D convolution with kernel sizes of (1,1), (3,3), and (5,5), respectively. The function Meanc is the average of the three-...
Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks Appl. Soft Comput. (2020) Z.Zhanget al. Multi-scale and multi-pooling sparse filtering: A simple and effective representation learning method for intelligent fault dia...
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Graph neural network-based anomaly detection in multivariate time series. In Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2–9 February 2021; Volume 35, pp. 4027–4035. [Google Scholar] Wang, X.; Lin, J.; Patel, N.; Braun, M. Exact variable-length anomaly ...
Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Adv. Neural Inf. Process. Syst. 2020, 33, 19314–19326. [Google Scholar] Kipf, T.N.; Welling, M. Semi-supervised classification with graph convolutional networks. In Proceedings of the ICLR, Toulon, ...