This study presents a novel approach for disaggregating aggregated train delays into primary and secondary components using Gated Graph Convolutional Networks (GatedGCNs). We develop a graph-based representation of railway traffic that captures complex spatiotemporal relationships and long-range dependencies....
Compared with traditional convolutional network models which can only be used in structured data computation, graph convolutional networks are special in capturing unstructured data. The road network structure in reality is typical unstructured data. Thus, the local feature of traffic data can be ...
Radiotherapy Target Contouring with ConvolutionalGated Graph Neural NetworkChun-Hung Chao 1 Yen-Chi Cheng 1 Hsien-Tzu Cheng 1 Chi-Wen Huang 1Tsung-Ying Ho 2 Chen-Kan Tseng 2 Le Lu 3 Min Sun 11 National Tsing Hua University2 Chang Gung Memorial Hospital3 National Institutes of Health Clinical ...
3.2. Gated convolutional neural networks A GCNN is a non-recurrent network alternative to capture long-term dependencies while avoiding sequential operations for better parallelizability. Thus, recurrent connections typically applied in RNNs are replaced by gated temporal convolutions. In general, convol...
The EEMD-GRU-GCN (Ensemble Empirical Mode Decomposition—Gated Recurrent Unit—Graph Convolutional Network) prediction algorithm is a complex, hybrid model that combines signal processing, recurrent neural networks, and graph-based neural networks to predict time series data. Below is a conceptual outlin...
3.1 Deep Convolutional Networks DCNN用在句子级表示是有效的。DCNN架构如图5所示,它用于将输入问题映射到固定大小的向量表示。输入 x = < s > , x 1 , x 2 , . . . , x n , < f > 50维GloVe,embedding加入CNN,池化+全连接+ReLU 获得vq 3.2 Gated Graph Neural Networks 门图神经网络通过基于...
Bearing remaining useful life prediction using self-adaptive graph convolutional networks with self-attention mechanism. Mech. Syst. Signal Process. 188, 110010 (2023). Article Google Scholar Yang, M., Huang, X., Huang, L. & Cai, G. Diagnosis of Parkinson’s disease based on 3D ResNet: ...
Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF) - leilin-research/GCGRNN
Language modeling with gated convolutional networks FangH. et al. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations ACM Transactions on Information Systems (2020) FengF. et al. Cross-GCN: Enhancing graph convolutional network with k-order feature interactions ...
After graph structure learning, GNNs can be adopted to embed the relational inductive bias for encoding the inter-variable structural dependencies. Among different kinds of GNNs, graph convolutional network (GCN) is a widely used technique for ICPSs [24], [25]. Through spectral graph convolution,...