Neural network optimisation has emerged as a transformative approach in microwave engineering, driving enhancements in both the accuracy and speed of electromagnetic (EM) simulations and circuit ...

In the rapidly evolving artificial intelligence landscape, one of the most persistent challenges has been the resource-intensive process of optimizing neural networks for deployment. While AI tools ...

Neural network pruning is a key technique for deploying artificial intelligence (AI) models based on deep neural networks (DNNs) on resource-constrained platforms, such as mobile devices. However, ...

A Queen’s research team has developed a new way to train AI systems so they focus on the bigger picture instead of specific, optimized data.

VFF-Net introduces three new methodologies: label-wise noise labelling (LWNL), cosine similarity-based contrastive loss (CSCL), and layer grouping (LG), addressing the challenges of applying a forward ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

“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 ...

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.

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 ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...

The new AI-powered functionalities consist of AI-managed Beamforming, AI-powered Outdoor Positioning, and a state-of-the-art AI model for instant coverage prediction, which complements Ericsson’s ...