Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation.
Yajing ZhengShanshan JiaZhaofei YuTiejun HuangJian K. LiuYonghong TianPublished in: Neural Networks (2020)
Keyphrases
- probabilistic inference
- markov random field
- spiking neural networks
- message passing
- exact inference
- efficient inference
- belief propagation
- graphical models
- biologically inspired
- approximate inference
- graph cuts
- higher order
- bayesian networks
- parameter estimation
- random fields
- influence diagrams
- partition function
- maximum a posteriori
- loopy belief propagation
- energy function
- pairwise
- mrf model
- feed forward
- belief networks
- image segmentation
- conditional random fields
- markov networks
- learning rules
- conditional probabilities
- factor graphs
- bayesian belief networks
- artificial neural networks
- neural network
- probabilistic graphical models
- probabilistic model
- object recognition
- junction tree
- computer vision