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 are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...

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 ...

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 ...

Dr. Jongkil Park and his team of the Semiconductor Technology Research Center at the Korea Institute of Science and Technology (KIST) have presented a new approach that mimics the brain's learning ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

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 ...

While nanotechnology combines the knowledge of physics, chemistry and engineering, AI has heavily relied on biological inspiration to develop some of its most effective paradigms such as neural ...