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New Theory Unveils the Black Box of Deep Learning

In the video presentation below (courtesy of Yandex) – “Deep Learning: Theory, Algorithms, and Applications” – Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, provides evidence in support of a new theory explaining how deep learning works. Tishby argues that deep neural networks learn according to a procedure called the “information bottleneck,” which he and two collaborators first described in purely theoretical terms in 1999. The idea is that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts. Striking new computer experiments by Tishby and his student Ravid Shwartz-Ziv reveal how this squeezing procedure happens during deep learning, at least in the cases they studied.

The Berlin workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. Participants were invited to present their recently published work as well as work in progress, and to share their vision and perspectives for the field.

 

 

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