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 ...

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

New research from the University of St Andrews, the University of Copenhagen and Drexel University has developed AI computational models that predict the degeneration of neural networks in amyotrophic ...

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 ...

AI systems now operate on a very large scale. Modern deep learning models contain billions of parameters and are trained on ...

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 of computational models inspired by the brain's layered organization, also ...