Recently, Graph Neural Networks (GNNs) have been successfully applied to modulation recognition ... from a graph perspective and propose a graph-powered modulation recognition framework. We first ...

Code for our SIGKDD'22 paper: "Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting". The code is developed with BasicTS, a PyTorch-based benchmark and ...

Deep neural networks will allow signal transfer of nerve cells to be analyzed in real time in the future. That is the result ...

An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. The nodes, also known as neurons, in these networks apply a nonlinear activation ...

Anomaly Detection,Convolutional Neural Network,Graph Neural Networks,Multivariate Time Series,Multivariate Time Series Data,Neural Network,Spatial Dependence,Temporal ...

Convolutional neural network structure. The first convolutional layer adopts a 32-layer convolution ... In this method, firstly, the recursive graph is used to convert the time series into ...

When deep neural networks, a form of AI that learns to discern patterns in data ... such as classifying either CIFAR-10 or ImageNet images, but not at the same time. In the future, he imagines ...