Kolmogorov-Arnold Networks

Kolmogorov-Arnold Networks are practical neural networks based on the Kolmogorov-Arnold representation theorem, which have shown promising early results. Whereas a normal neural network has a fixed activation function, such as a sigmoid and learn the weights between nodes; Kolmogorov-Arnold Networks work by learning the activation function with fixed weights. They use simplified forms of the more complex non-linear functions required by the theorem to create networks with only a few layers that can efficiently solve complex problems. The network is an example of the use of non-linear diversity.