Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?
Safa MessaoudMaghav KumarAlexander G. SchwingPublished in: CVPR Workshops (2020)
Keyphrases
- graphical models
- probabilistic inference
- exact inference
- reinforcement learning
- map inference
- factor graphs
- bayesian networks
- belief networks
- belief propagation
- probabilistic graphical models
- random variables
- undirected graphical models
- probabilistic model
- approximate inference
- loopy belief propagation
- efficient inference algorithms
- conditional random fields
- statistical inference
- nonparametric belief propagation
- structure learning
- directed graphical models
- markov networks
- probabilistic networks
- message passing
- graph structure
- conditional independence
- structured prediction
- markov logic networks
- machine learning
- image segmentation
- graphical structure
- statistical relational learning
- efficient learning
- bayesian inference
- gaussian graphical models
- state space
- random fields
- gaussian process
- articulated body
- parameter learning