The systems are further reduced to continuous-time neural networks (NNs) with interval matrix uncertainties, and a unified method is developed to solve the stability of delayed NNs and MNNs. Stability ...

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 ...

These models aim to leverage the unique capabilities of quantum computers, such as superposition, entanglement, and interference, to overcome limitations faced by classical neural networks (NNs).

It is worth noting neuromorphic computing is not synonymous with the deep neural networks (DNNs) already in wide use within industry – although there is growing interest in using the former to ...

In this paper, we present an ASD classification network defined as CNNG by combining of convolutional neural network (CNN) and gate recurrent unit (GRU). First, CNNG extracts the 3D spatial features ...

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