Handling dropout probability estimation in convolution neural networks using meta-heuristics-Reference-Cited by-同舟云学术

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Handling dropout probability estimation in convolution neural networks using meta-heuristics

Published:2017-06-23 Issue:18 Volume:22 Page:6147-6156
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

de Rosa Gustavo H.,Papa João P.^ORCID,Yang Xin-S.

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/article/10.1007/s00500-017-2678-4/fulltext.html

Reference32 articles.

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2. Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

3. Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. ACM, pp 160–167

4. Cox D, Pinto N (2011) Beyond simple features: a large-scale feature search approach to unconstrained face recognition. In: Proceedings of the IEEE international conference on automatic face gesture recognition and workshops, pp 8–15

5. Dahl GE, Sainath TN, Hinton GE (2013) Improving deep neural networks for lVCSR using rectified linear units and dropout. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 8609–8613

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