Better paths yield better AI
Deep Learning (DL) performs classification tasks using a series of layers. To effectively execute these tasks, local decisions are performed progressively along the layers. But can we perform an all-encompassing decision by choosing the most influential path to the output rather than performing these decisions locally?
In an article published today in Scientific Reports, researchers from Bar-Ilan University in Israel answer this question with a resounding "yes". Pre-existing deep architectures have been improved by updating the most influential paths to the output.
"One can ...







