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).

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 ...

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.

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 ...

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 ...