.png){kind=icon}
返回
Neural network implementation of inference on binary Markov random fields with probability coding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2016.12.025.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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). (C) 2016 Elsevier Inc. All rights reserved.
Keyword:
Markov random fields
Approximate inference
Neural network implementation
Hopfield network
Probability coding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
Imine-linked micron-network polymers with high polyethylene glycol uptake for shaped-stabilized phase change materials.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Vapor phase assembly of benzotriazole and octadecylamine complex films on aluminum alloy surface
PREAI
分享收藏
查看更多内容