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Estimation of Deep Neural Networks Capabilities Using Polynomial Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

Abstract

Currently very popular trend in artificial intelligence is the use of deep neural networks. The power of such networks are very large, but the main difficulty is learning these networks. The article presents a analysis of deep neural network nonlinearity with polynomial approximation of neuron activation functions. It is shown that nonlinearity grows exponentially with the depth of the neural network. The effectiveness of the approach is demonstrated by several experiments.

This work was supported by the National Science Centre, Cracow, Poland under Grant No. 2013/11/B/ST6/01337.

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Correspondence to Pawel Rozycki .

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Rozycki, P., Kolbusz, J., Korostenskyi, R., Wilamowski, B.M. (2016). Estimation of Deep Neural Networks Capabilities Using Polynomial Approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-39378-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

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