Scientists may now be one step closer to understanding the inner logic of artificial intelligence (AI) models used for genomics thanks to a new tool from a group at Simons Center for Quantitative ...

In this work we apply deep neural networks to find the non-equilibrium steady state solution to correlated open quantum many-body systems. Motivated by the ongoing search to find more powerful ...

Although deep neural networks (DNNs) have revolutionized modern machine learning 1,2, a fundamental theoretical understanding of why they perform so well remains elusive 3,4. One of their most ...

The k* distribution method, developed by researchers from Kyushu University, allows clear visualization and evaluation of how a neural network interprets data. Fukuoka, Japan— Deep neural networks are ...

Unlike their more modern large language model counterparts, artificial neural networks require human input to learn and function. ANNs have been around since the 1950s. They started taking hold in ...

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance. However, in new research published in APL Machine Learning, I have used a phenomenon ...

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work ...

Artificial Deep Neural Networks (DNNs) and, more recently, large-scale foundation models have made breakthrough progress drawing loose structural and ...