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 ...

Information Theory Meets Deep Neural Networks: Theory and Applications. The previous volume can be viewed here: Volume I Deep Neural Networks (DNNs) have become one of the most popular research ...

The ability to analyze the brain's neural connectivity is emerging as a key foundation for brain-computer interface (BCI) ...

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 ...

Recent advances in neural network methodologies have significantly reshaped the fields of electrical tomography and moisture analysis. By integrating artificial neural networks (ANNs) for both image ...

Last week we described the next stage of deep learning hardware developments in some detail, focusing on a few specific architectures that capture what the rapidly-evolving field of machine learning ...

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 ...

The type of artificial intelligence known as a neural network can be trained to complete tasks once thought to be exclusive to humans, such as driving a car, creating visual art, or composing a heavy ...

Dublin, Oct. 20, 2025 (GLOBE NEWSWIRE) -- The "Neural Network Market Size, Share & Trends Analysis Report by Type (Data Mining & Archiving, Analytical Software), Deployment (on-premises, Cloud), ...