Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...
Fig. 1 shows the mapping of points from the training sample in the coordinates of the two main features – u1 and u2. The color of the point corresponds to the class (red – 0, aqua – 1). From the ...
Neural networks have emerged as a pivotal technology in enhancing the precision and reliability of depth of anaesthesia (DoA) monitoring. By integrating advanced signal processing techniques with ...
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Overparameterized neural networks: Feature learning precedes overfitting, research finds
Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...
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Taming chaos in neural networks: A biologically plausible way
A new framework that causes artificial neural networks to mimic how real neural networks operate in the brain has been ...
Journal of Housing and the Built Environment, Vol. 18, No. 2 (2003), pp. 159-181 (23 pages) In recent years, the neural network modelling technique has become a serious alternative to and extension of ...
It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...
Weight decay and weight restriction are two closely related, optional techniques that can be used when training a neural network. This article explains exactly what weight decay and weight restriction ...
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