Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?
Safa MessaoudMaghav KumarAlexander G. SchwingPublished in: CoRR (2020)
Keyphrases
- graphical models
- probabilistic inference
- exact inference
- reinforcement learning
- map inference
- belief networks
- factor graphs
- belief propagation
- bayesian networks
- probabilistic graphical models
- efficient inference algorithms
- loopy belief propagation
- approximate inference
- random variables
- undirected graphical models
- probabilistic model
- statistical inference
- directed graphical models
- nonparametric belief propagation
- structure learning
- conditional random fields
- relational dependency networks
- probabilistic networks
- markov networks
- structured prediction
- bayesian belief networks
- state space
- learning algorithm
- graph structure
- conditional independence
- gaussian graphical models
- message passing
- statistical relational learning
- markov logic networks
- graphical structure
- conditional dependencies
- efficient learning
- dynamic bayesian networks
- partition function
- graph cuts
- junction tree
- parameter learning
- bayesian inference