It now appears that neural nets may be the next frontier in the advance of computing technology as a whole. But what are ...

Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for ...

Abstract: The aim of this research is to quickly detect cross-site scripting (XSS) attacks on the internet based on relationship diagram convolutional networks ... and fed into the fully connected ...

When two-dimensional electron systems are subjected to magnetic fields at low temperatures, they can exhibit interesting ...

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A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

Convolutional Neural Networks (ConvNets or CNNs) are a class of neural networks algorithms that are mostly used in visual recognition tasks such as image classification, object detection, and image ...

School of Computer Science, Wuhan University, Wuhan 430072, P. R. China ...

CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of tasks, including foundation models (e.g., CLIP ...