Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference.
Yarin GalZoubin GhahramaniPublished in: CoRR (2015)
Keyphrases
- variational inference
- convolutional neural networks
- exponential family
- bayesian inference
- posterior distribution
- mixture model
- dirichlet process
- maximum likelihood
- probabilistic model
- gaussian process
- variational methods
- topic models
- graphical models
- probabilistic graphical models
- closed form
- latent dirichlet allocation
- log likelihood
- statistical models
- missing values
- posterior probability
- probability distribution
- density estimation
- latent variables
- em algorithm
- bayesian framework
- hyperparameters
- gaussian processes
- factor graphs
- parameter estimation
- text mining
- approximate inference
- unsupervised learning
- markov chain monte carlo
- hidden variables
- expectation maximization
- message passing
- model selection
- markov networks
- gaussian distribution
- exact inference
- order statistics
- prior information
- belief propagation
- generative model
- machine learning