Deep Networks Are Kernel Machines

Feb 22, 07:00PM PDT(03:00AM GMT).
  • Free 271 Attendees
The event is hosted by San Francisco Bay ACM Chapter.

Deep learning successes are often attributed to its ability to automatically discover new representations of the data, rather than relying on handcrafted features like other learning methods. In this talk, however, Pedro will show that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines, a learning method that simply memorizes the data and uses it directly for prediction via a similarity function (the kernel).

Pedro Domingos

Professor of computer science at the University of Washington and the author of "The Master Algorithm", the worldwide bestseller introducing machine learning to a broad audience. He is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI, and a Fellow of the AAAS and AAAI.
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