The structure of KANs is similar to that of conventional neural networks. The weights do not have a fixed numerical value, however. Instead they correspond to a function: w(x).

How reliable is artificial intelligence, really? An interdisciplinary research team at TU Wien has developed a method that ...

That takes a huge amount of time and energy—during his NeurIPS talk, Petersen said that training his networks takes hundreds of times longer than training conventional neural networks on GPUs.

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.

Researchers have long been interested in how humans and animals make decisions by focusing on trial-and-error behavior ...

My neural network did well at simulating human perception of the famous Necker cube and Rubin’s vase illusions – and in fact better than some much larger conventional neural networks used in ...

Researchers from the University of Cambridge have built a neural network they claim can solve the three-body problem much faster than a conventional computer, giving astronomers a leg up in ...

A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

Their big news is that their network provides accurate solutions at a fixed computational cost and up to 100 million times faster than a state-of-the-art conventional solver. They start with a ...

Researchers trained a generative AI neural network to run the 1993 video game Doom instead of a conventional video game engine.