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May 21, Tuesday
12:00 – 13:00

One of the most significant recent developments in machine learning has been the resurgence of ``deep learning'', usually in the form of artificial neural networks. These systems are based on a multi-layered architecture, where the input goes through several transformations, with higher-level concepts derived from lower-level ones. Thus, these systems are considered to be particularly suitable for hard AI tasks, such as computer vision and language processing. Nevertheless, despite decades of research,a major caveat of deep learning is - and always has been - its strong reliance on heuristic methods. I will describe a new, provably efficient, algorithm for learning deep neural networks.

Joint work with Roi Livni and Ohad Shamir.