Using Graphical Models as Explanations in Deep Neural Networks.
Franck LeMudhakar SrivatsaKrishna Kesari ReddyKaushik RoyPublished in: MASS (2019)
Keyphrases
- graphical models
- neural network
- belief propagation
- probabilistic inference
- probabilistic model
- probabilistic graphical models
- random variables
- bayesian networks
- structure learning
- approximate inference
- graph structure
- markov networks
- conditional random fields
- exact inference
- belief networks
- gaussian graphical models
- deep architectures
- map inference
- statistical inference
- fuzzy logic
- loopy belief propagation
- factor graphs
- message passing
- deep learning
- statistical relational learning
- undirected graphical models
- structural learning
- conditional independence
- chain graphs
- directed acyclic