Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

Neural networks have emerged as a powerful framework for addressing complex problems across numerous scientific domains. In particular, the interplay between neural network models and constraint ...

Recent advances at the intersection of neural networks and inverse scattering problems have transformed traditional approaches to imaging and material characterisation.

A learning process tunes these connection strengths via trial and error, attempting to maximize the neural network’s performance at solving some problem. The goal might be to match input data and make ...

This quantum-inspired enhancement, known as a tensorized neural network, is helping companies realize savings in training for a wide range of applications, including self-driving vehicle ...

An RIT scientist has been tapped by the National Science Foundation to solve a fundamental problem that plagues artificial neural networks. Christopher Kanan, an assistant professor in the Chester F.

For those areas where the practical applications of neural networks are clear, the problems backtracking the answer can be a deal-killer. For instance, if researchers in chemistry use a neural network ...

Deep neural networks will move past their shortcomings without help from symbolic artificial intelligence, three pioneers of deep learning argue in a paper published in the July issue of the ...

Convolutional neural networks One of the key components of most deep learning–based computer vision applications is the convolutional neural network (CNN).

Artificial Neural Networks: Learning by Doing Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.