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

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

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 ...

This study presents a transfer learning–based method for predicting train-induced environmental vibration. The method applies data fusion to combine physics-based numerical simulations and limited ...

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

A new study suggests that the brain processes information with high efficiency by synchronizing the physical wiring of neural ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

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 ...

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