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 ...

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 ...

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 ...

Emergence of new applications and use cases: Neural networks are being applied to an increasingly diverse range of applications, including computer vision, natural language processing, fraud detection ...

“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 ...

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...

Investing.com -- WiMi Hologram Cloud Inc. (NASDAQ:WIMI) stock surged 11.3% on Monday after the company announced the launch of its independently developed Quantum Convolutional Neural Network for ...

In a paper published in the journal Nature, researchers developed a recurrent, transformer-based neural network to decode the surface code, a leading quantum error ...