Arm has announced the availability of the first public specification drafted around its Chiplet System Architecture (CSA), a set of system partitioning and chiplet connectivity standards harnessed in ...

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 ...

Inspired by microscopic worms, Liquid AI’s founders developed a more adaptive, less energy-hungry kind of neural network. Now the MIT spin-off is revealing several new ultraefficient models.

“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 ...

The autoencoder has the same number of inputs and outputs (9) as the demo program, but for simplicity the illustrated autoencoder has architecture 9-2-9 (just 2 hidden nodes) instead of the 9-6-9 ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

While neural networks sprint through data, their architecture makes it difficult to trace the origin of errors that are obvious to humans — like confusing a Converse high-top with an ankle boot — ...

While neural networks sprint through data, their architecture makes it difficult to trace the origin of errors that are obvious to humans -- like confusing a Converse high-top with an ankle boot ...

An artificial neural network called an autoencoder is used to learn effective codings for unlabeled input (unsupervised learning). By teaching the network to disregard irrelevant data (or “noise”), ...