Graph neural networks are very powerful tools. They have already found powerful applications in domains such as route planning, fraud detection, network optimization, and drug research.

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, ...

Abstract “Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months ...

The "Hello World" of image classification is a convolutional neural network (CNN) applied to the MNIST digits dataset. A good way to see where this article is headed is to take a look at the ...

As the number of convolutional layers connected in series increases, so does the level of detail that can be recognized. The process starts with simple object properties, such as edges or points, ...

How do convolutional neural networks work? Convolutional neural networks work by extracting features from input data through convolutional layers and learning to classify the input data through ...

Those insights guided the design of the deep net by Yamins and his colleagues. Their deep net had hidden layers, some of which performed a “convolution” that applied the same filter to every portion ...