Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning?
Safa MessaoudMaghav KumarAlexander G. SchwingPublished in: CVPR (2020)
Keyphrases
- graphical models
- probabilistic inference
- exact inference
- reinforcement learning
- map inference
- belief propagation
- bayesian networks
- factor graphs
- belief networks
- loopy belief propagation
- efficient inference algorithms
- nonparametric belief propagation
- approximate inference
- undirected graphical models
- probabilistic graphical models
- probabilistic model
- random variables
- probabilistic networks
- structure learning
- statistical inference
- directed graphical models
- conditional random fields
- efficient learning
- conditional independence
- partition function
- markov networks
- dynamic bayesian networks
- bayesian inference
- conditional dependencies
- message passing
- state space
- statistical relational learning
- markov logic networks
- graphical structure
- bayesian belief networks
- graph structure
- parameter learning
- machine learning
- relational dependency networks
- stereo matching
- random fields
- human body
- markov random field