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

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• 作者：Zhaofei Yu^a ; Feng Chen ; ^a ; ^b ; ^{chenfeng@mail.tsinghua.edu.cn} ; Jianwu Dong^a
• 关键词：Markov random fields ; Approximate inference ; Neural network implementation ; Hopfield network ; Probability coding
• 刊名：Applied Mathematics and Computation
• 出版年：2017
• 出版时间：15 May 2017
• 年：2017
• 卷：301
• 期：Complete
• 页码：193-200
• 全文大小：1286 K
• 卷排序：301

文摘

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).