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
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 ...
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 ...
On the other hand, pooling layers perform down-sampling on the image in order to minimise the number of learnable param- eters. This study implements one-dimensional convolution layers to process sequence data. The Transformer neural network architecture has had a significant impact in the field ...
15. The system of claim 14, wherein the first machine learning model further comprises: a third layer corresponding to a max-pooling operation. 16. The system of claim 11, wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the ...
The MATLAB Deep Learning Toolbox was applied in this study for DEM enhancement. We used U-Net structure to design the CNN model. In U-Net structure, the initial series of convolutional layers are interspersed with max pooling layers, successively decreasing the resolution of the input image. Th...
3.1. Three-Dimensional Convolutional Neural Network Compared with the 2-D CNNs, the 3-D CNNs have better performance in time information modeling and spatiotemporal feature learning. In the 3-D CNNs, the convolution and pooling processes are completed in the space-time dimension, while in the...
Networks (SENet)29, which learns the significance of feature channels through global average pooling and fully connected layers. In this approach, each feature channel is assigned a weight based on its importance, enabling the neural network to prioritize specific proper feature channels pertinent to ...
In some implementations, combining the convolution outputs includes: summing, for each of the multiple filters, the convolution outputs obtained for different channels using the filter to generate summed outputs corresponding to different time periods; and pooling, for each of the multiple filters, the...
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...