GNNs extend the foundational ideas of Convolutional Neural Networks (CNNs) to graph data. While CNNs capture spatial locality in grid-like data (for example, images) through convolutional kernels, ...

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, ...

The team develops a new model, GCM, which captures the interactions among multiple user behaviors via graph neural networks, ... Graph convolution machine for context-aware recommender system.

Neural Networks, fundamentally, are computer systems designed to mimic the human brain. They have the capacity to learn, understand, and interpret complex patterns, making them a crucial aspect of ...

Convolutional Neural Networks for MNIST Data Using PyTorch. Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to ...

A Convolutional Neural Network (CNN) is a form of artificial intelligence that plays a key role in the AI ecosytem due to its ability to analyze and understand visual data. The need to decipher ...

Other than giving us an appreciation how little difference going eight miles an hour over the speed limit makes, that ETA service is a remarkable invention — and one that takes a hell of a lot of ...