Throughout their everyday lives, humans are typically required to make a wide range of decisions, which can impact their well ...

Major Types of Neural Nets There are many different individual neural net projects, but they can generally be split into a few key families, by form and function.

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...

A common objective for neural networks is to find a mathematical function, or curve, that best connects certain data points. The closer the network can get to that function, the better its predictions ...

Five DECADES of research into artificial neural networks have earned Geoffrey Hinton the moniker of the Godfather of artificial intelligence (AI). Work by his group at the University of Toronto ...

In their work, Liu and his colleagues compared the KANs they developed with conventional neural networks, known as multilayer perceptrons (MLPs). In an initial test, they used various known functions, ...

To address the exponential synchronization problem in coupled neural networks. Utilizing delay-compensatory impulsive control and Razumikhin-type inequalities. The Lyapunov function for coupled neural ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the ...

In this study, we investigate a new neural network method to solve Volterra and Fredholm integral equations based on the sine-cosine basis function and extreme learning machine (ELM) algorithm.