Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Most of the challenge of performing neural network quantile regression is finding good values for the four key hyperparameters: number hidden nodes, learning rate, quantile rate, max epochs.

This study presents valuable computational findings on the neural basis of learning new motor memories and the savings using recurrent neural networks. The evidence supporting the claims of the ...

Typically, when engineers build machine learning models out of neural networks — composed of units of computation called artificial neurons — they tend to stop the training at a certain point, called ...

By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.