As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept ...

A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the ...

People's decisions are known to be influenced by past experiences, including the outcomes of earlier choices. For over a century, psychologists have been trying to shed light on the processes ...

Researchers have used machine learning to create a model that simulates reactive processes in organic materials and conditions. Researchers from Carnegie Mellon University and Los Alamos National ...

Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

Especially when it comes to manufacturing, problem-solving is an art. Every day, companies within this industry face challenges that test their processes, products and, ultimately, their bottom line.

Recently, model-based reinforcement learning has been considered a crucial approach to applying reinforcement learning in the physical world, primarily due to its efficient utilization of samples.