Like climbing a mountain via the shortest possible path, improving classification tasks can be achieved by choosing the most influential path to the output, and not just by learning with deeper networks. Deep Learning (DL) performs classification tasks using a series of layers. To effectively exe
Researchers have improved deep learning by selecting the most efficient overall path to the output, leading to a more effective AI without added layers.
Like climbing a mountain via the shortest possible path, improving classification tasks can be achieved by choosing the most influential path to the output, and not just by learning with deeper networks. Deep Learning 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?, researchers from Bar-Ilan University in Israel answer this question with a resounding “yes.
Like climbing a mountain via the shortest possible path, improving classification tasks can be achieved by training the most influential path to the output, and not just by learning with deeper networks. Credit: Prof. Ido Kanter, Bar-Ilan University“One can think of it as two children who wish to climb a mountain with many twists and turns.
“This discovery can pave the way for better enhanced AI learning, by choosing the most significant route to the top,” added Yarden Tzach, a PhD student and one of the key contributors to this work.This exploration of a deeper comprehension of AI systems by Prof. Kanter and his experimental research team, led by Dr. Roni Vardi, aims to bridge between the biological world and
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