Speech recognition, handwriting recognition, face recognition: just a few of the many tasks that we as humans are able to quickly solve but which present an ever increasing challenge to computer ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

University of Illinois professor Klara Nahrstedt received $275,000 from the National Science Foundation to develop streaming ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

Earth Scientists have used machine learning for at least three decades and the applications span is large, from remote sensing to analysis of well log data, among many others. Although machine ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Neural networks are the backbone of algorithms that predict consumer demand, estimate freight arrival time, and more. At a high level, they're computing systems loosely inspired by the biological ...

A team at the University of California, San Diego has redesigned how RRAM operates in an effort to accelerate the execution ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Future Market Insights (FMI) projects the Neural Processors Market to grow from USD 176 million in 2025 to USD 1,010 million by 2035, advancing at a 19.1% CAGR. This surge is being driven by the ...