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International Conference on Algorithmic Learning Theory

ALT 2011: Algorithmic Learning Theory pp 18–36Cite as

On the Expressive Power of Deep Architectures

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6925))

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 difficult 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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Authors and Affiliations

  1. Dept. IRO, Université de Montréal, Montréal, QC, H3C 3J7, Canada

    Yoshua Bengio & Olivier Delalleau

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  1. Yoshua Bengio
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  2. Olivier Delalleau
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  1. Department of Computer Science, University of Helsinki, (Gustaf Hällströmin katu 2b), P.O. Box 68, 00014, Helsinki, Finland

    Jyrki Kivinen  & Esko Ukkonen  & 

  2. Department of Computing Science, University of Alberta, T6G 2E8, Edmonton, AB, Canada

    Csaba Szepesvári

  3. Division of Computer Science, Hokkaido University, N-14, W-9, 060-0814, Sapporo, Japan

    Thomas Zeugmann

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Bengio, Y., Delalleau, O. (2011). On the Expressive Power of Deep Architectures. In: Kivinen, J., Szepesvári, C., Ukkonen, E., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2011. Lecture Notes in Computer Science(), vol 6925. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24412-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-24412-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

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