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 ...

Dr. Chris Hillman, Global AI Lead at Teradata, joins eSpeaks to explore why open data ecosystems are becoming essential for enterprise AI success. In this episode, he breaks down how openness — in ...

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 ...

Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the ...

Emergence of new applications and use cases: Neural networks are being applied to an increasingly diverse range of applications, including computer vision, natural language processing, fraud detection ...