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    On the Expressive Power of Deep Architectures

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    Deep architectures are families of functions corresponding to deep circuits. Deep Learning algorithms are based on parametrizing such circuits and tuning their parameters so as to approximately optimize some training objective. Whereas it was thought too dicult to train deep architectures, several successful algorithms have been proposed in recent years. We review some of the theoretical motivations for deep architectures, as well as some of their practical successes, and propose directions of investigations to address some of the remaining challenges.

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    Description

    Title : On the Expressive Power of Deep Architectures
    Author(s) : Yoshua Bengio, Olivier Delalleau
    Abstract : Deep architectures are families of functions corresponding to deep circuits. Deep Learning algorithms are based on parametrizing such circuits and tuning their parameters so as to approximately optimize some training objective. Whereas it was thought too dicult to train deep architectures, several successful algorithms have been proposed in recent years. We review some of the theoretical motivations for deep architectures, as well as some of their practical successes, and propose directions of investigations to address some of the remaining challenges.
    Keywords : deep learning

    Subject : unspecified
    Area : Computer Science
    Language : English
    Year : 2011

    Affiliations University of Montreal
    Conference_title : 22nd International Conference on Algorithmic Learning Theory
    Url : http://www.iro.umontreal.ca/~lisa/pointeurs/ALT2011.pdf

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