Evaluating Approximate Inference in Bayesian Deep Learning.
Andrew Gordon WilsonPavel IzmailovMatthew D. HoffmanYarin GalYingzhen LiMelanie F. PradierSharad VikramAndrew Y. K. FoongSanae LotfiSebastian FarquharPublished in: NeurIPS (Competition and Demos) (2021)
Keyphrases
- approximate inference
- deep learning
- bayesian networks
- graphical models
- probabilistic inference
- expectation propagation
- exact inference
- belief propagation
- parameter estimation
- deep belief networks
- message passing
- gaussian process
- loopy belief propagation
- machine learning
- latent variables
- unsupervised learning
- probabilistic graphical models
- conditional random fields
- restricted boltzmann machine
- posterior distribution
- maximum likelihood
- probabilistic model
- mental models
- free energy
- weakly supervised
- bayesian inference
- data mining
- random variables
- conditional probabilities
- em algorithm
- multiscale
- three dimensional