The paper uses the model DCGCN, for detail architecture please refer to the TACL19 paper Densely Connected Graph Convolutional Network for Graph-to-Sequence Learning. Codes are adapted from the repo ...

Graph Convolutional Network,Graphical Output,Hybrid Model,Malignant Lesions,Melanoma,Pointwise Convolution,Pre-trained Convolutional Neural Network,Precision Score,ResNet-50 Model,Residual Block,Skin ...

To solve abovementioned problems, a novel deep learning-based spatio-temporal graph convolutional neural network (STGCN) is developed for intelligent fault diagnosis of wind turbines in this article.

MacOS Quick Look visualizer for neural network/machine learning based on Lutz Roeder's Netron.

School of Information Science and Engineering, Hebei University of Science and Technology, 26 Yuxiang Street, Shijiazhuang, Hebei Province 050018, P. R. China ...

The Graph offers access to competitive and cost-efficient decentralized data sets. The network boasts a 99.99% uptime and 24/7 availability. Central to The Graph’s operations are subgraphs, APIs that ...

What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...

The waveform graph was sampled from the first patient in the CHB-MIT EEG dataset ... We introduce a recurrent spiking neural network EESNN (EEG-based recurrent convolutional spiking neural network) ...

There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.