Various generative AI models, including transformer-based models, GANs, and diffusion models, are trained through different processes involving large-scale data, neural networks, and methods like ...

An alternative to manual design is “neural architecture search” (NAS), a series of machine learning techniques that can help discover optimal neural networks for a given problem.

The number of scientific papers that rely on AI has quadrupled, and the scope of problems AI can tackle is expanding by the ...

Developing the ideal neural architecture for AI models is a complex and ongoing process. There is no one-size-fits-all solution, as different tasks and datasets require different architectures.

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 without ...

Stable Diffusion (from Stability AI) and Imagen, both released in 2022, used variations of an architecture called a convolutional neural network (CNN), which is good at analysing grid-like data ...

Next, we will look at a variety of neural network styles that learn from and also move beyond the perceptron model. Feedforward networks They offer a much higher degree of flexibility than ...

Choosing what stimulus to focus on, a.k.a. attention, is also the main mechanism behind another neural network architecture, the transformer, which has become the heart of large language models ...