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Integrating Deep Learning and Bayesian Reasoning

  • Sin Yin Tan
  • Wooi Ping CheahEmail author
  • Shing Chiang Tan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)

Abstract

Deep learning (DL) is an excellent function estimator which has amazing result on perception tasks such as visualization recognition and text recognition. But, its inner architecture acts as a black box, because the users cannot understand why such decisions are made. Bayesian reasoning (BR) provides explanation facility and causal reasoning in terms of uncertainty which is able to overcome demerit of DL. This paper is to propose a framework for the integration of DL and BR by leveraging their complementary merits based on their inherent internal architecture. The migration from deep neural network (DNN) to Bayesian network (BN) involves extracting rules from DNN and constructing an efficient BN based on the rules generated, to provide intelligent decision support with accurate recommendations and logical explanations to the users.

Keywords

Black box of deep learning Bayesian reasoning Integration Rule extraction 

Notes

Acknowledgments

This work was supported by the Fundamental Research Grant Scheme (FRGS) from the Ministry of Education and Multimedia University, Malaysia (Project ID: FRGS/1/2018/ICT02/MMU/02/1).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sin Yin Tan
    • 1
  • Wooi Ping Cheah
    • 1
    Email author
  • Shing Chiang Tan
    • 1
  1. 1.Faculty of Information Science and TechnologyMultimedia UniversityMelakaMalaysia

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