Many conventional computer architectures are ill-equipped to meet the computational demands of machine learning-based models. In recent years, some engineers have thus been trying to design ...

The default mode network (DMN) is a set of interconnected brain regions known to be most active when humans are awake but not ...

Jonathan EnudemeJonathan Enudeme Imagine finding yourself lost in a foreign land where no one speaks English or your native language. The streets are unfamiliar, and every turn leads you deeper into ...

Researchers at FORTH have developed a new type of artificial neural network (ANN) that incorporates features of biological ...

A Generative Adversarial Network (GAN) is a type of machine learning model that’s used to generate fake data that resembles ...

The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

This paper proposes an improved gray-box modeling technique that combines the accuracy of the neural network (NN) technique with the physical ... The proposed approach was applied to GaN High Electron ...

GaN is especially well-established in low-power applications ... However, the spacing between devices is still constrained by the need for adequate isolation. Vertical device architectures allow ...

“I must correct that,” said DPM Gan, who is also Trade and Industry Minister. Singapore has developed “quite an elaborate” code of conduct for how it should develop and deploy AI in an ...

introduced the concept of Generative Adversarial Networks (GANs), where two neural networks, i.e., the generator and ... Subsequent breakthroughs in Transformer-based architectures brought predictive ...

Source code for the paper "Automatic Fused Multimodal Deep Learning for Plant Identification" (Alfreds Lapkovskis, Natalia Nefedova & Ali Beikmohammadi, 2024) ...

Learn More A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time ...