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Neural network implementation of inference on binary Markov random fields with probability coding

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  • Yu, Zhaofei
  • Chen, Feng
  • Dong, Jianwu

Abstract

Markov random fields (MRF) underpin the solution to many problems in computational neuroscience. However, how the inference for MRF could be implemented with neural network is still an important open question. In this paper, we build the relationship between inference equation of MRF and the dynamic equation of the Hopfield network with probability coding. We prove that the membrane potential in the Hopfield network varying with respect to time can implement marginal probabilities inference on binary MRF. Theoretical analysis and experimental results show that our neural network can get comparable results as that of loopy belief propagation (LBP).

Suggested Citation

  • Yu, Zhaofei & Chen, Feng & Dong, Jianwu, 2017. "Neural network implementation of inference on binary Markov random fields with probability coding," Applied Mathematics and Computation, Elsevier, vol. 301(C), pages 193-200.
  • Handle: RePEc:eee:apmaco:v:301:y:2017:i:c:p:193-200
    DOI: 10.1016/j.amc.2016.12.025
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    References listed on IDEAS

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    1. Ahmadvand, Ali & Daliri, Mohammad Reza, 2015. "Improving the runtime of MRF based method for MRI brain segmentation," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 808-818.
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