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 ...

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 ...

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

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 ...

Accurately forecasting resource demand within the supply chain has never been easy, particular given the constantly changing nature of the data over periods of time. What may have been true in ...

“The KL1140 is our response to the challenges of scaling LLMs in the cloud alone,” said founder and Chief Executice Albert ...