New research from the University of St Andrews, the University of Copenhagen and Drexel University has developed AI ...

Neural network-based branch prediction techniques represent a significant advancement in processor architecture, where machine learning models replace traditional, heuristic-based mechanisms to ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Encoding individual behavioral traits into a low-dimensional latent representation enables the accurate prediction of decision-making patterns across distinct task conditions.

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

AI systems now operate on a very large scale. Modern deep learning models contain billions of parameters and are trained on large datasets. Therefore, they produce strong accuracy. However, their ...

Multi area RNN models fitted to in-vivo cortical activity predict behavioral changes induced by optogenetic perturbations, if biologically informed connectivity constraints on the optogenetically ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

A team led by Guoyin Yin at Wuhan University and the Shanghai Artificial Intelligence Laboratory recently proposed a modular machine learning ...

Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development ...

Crystal ball: In its first hurricane season, Google's Deepmind AI framework not only matched decades of human expertise but surpassed the output of two of the world's most advanced supercomputer ...