"Nonlinear functions are critical for training deep neural networks," says Liang Feng, Professor in Materials Science and Engineering (MSE) and in Electrical and Systems Engineering (ESE), and the ...

Researchers from Hewlett Packard Labs, Indian Institutes of Technology Madras, Microsoft Research, and University of Michigan built an AI acceleration platform based on heterogeneously integrated ...

A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive processors” was published by researchers at UCLA. “Nonlinear computation is ...

Postdoctoral fellow Tianwei Wu (left) and Professor Liang Feng (right) in the lab, demonstrating some of the apparatus used to develop the new, light-powered chip. Penn Engineers have developed the ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks. Artificial intelligence ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Researchers at the University of Pennsylvania have developed a programmable photonic chip. This innovation could transform artificial intelligence's machine learning by using light for computations.

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...