Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

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

Using machine learning and math, a Brigham Young University student improved a key tool firefighters rely on during wildfire ...

Brigham Young University graduate Jane Housley's research could help make a widely used wildfire modeling tool faster and ...

Using machine learning and math, a BYU student improved a key tool firefighters rely on during wildfire season ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

Neural nets get a whole lot more complicated than this, but this is the essential structure: different places within a network are represented by nodes (circles) and connections between them ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...

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).