Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation.
Yajing ZhengZhaofei YuShanshan JiaJian K. LiuTiejun HuangYonghong TianPublished in: CoRR (2019)
Keyphrases
- probabilistic inference
- markov random field
- spiking neural networks
- message passing
- exact inference
- efficient inference
- belief propagation
- graphical models
- biologically inspired
- approximate inference
- markov networks
- influence diagrams
- bayesian networks
- graph cuts
- maximum a posteriori
- higher order
- mrf model
- parameter estimation
- random fields
- image segmentation
- loopy belief propagation
- factor graphs
- conditional probabilities
- energy function
- learning rules
- bayesian belief networks
- pairwise
- probabilistic graphical models
- belief networks
- conditional random fields
- feed forward
- artificial neural networks
- partition function
- feature selection
- machine learning
- feature space
- junction tree
- high dimensional
- probability distribution
- bayesian framework
- neural network