Blending ‘old-fashioned’ logic systems with the neural networks that power large language models is one of the hottest trends ...

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language ...

The Defense Advanced Research Projects Agency awarded Professor Jie Gu and co-PIs from the University of Minnesota and Duke University up to $3.8 million through the Scalable Analog Neural-networks ...

Biological neural networks are immensely complex systems underlying all aspects of cognition and behavior. Despite significant advances in neuroscience, a ...

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 ...

Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...

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 ...

Photons are fast, stable, and easy to manipulate on chips, making photonic systems a promising platform for QCNNs. However, ...

This year, competition participants created a service for detecting chest pathologies in CT scans, a smart map of the water supply network and a program that detects damage to trees and shrubs online.

Our poster boards measure 4 feet (48 inches) in height and 8 feet (96 inches) in width. Judging criteria include categories such as clarity and rationale, rigor and methods, experimental design, ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...