Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
Danilo Jimenez RezendeShakir MohamedDaan WierstraPublished in: ICML (2014)
Keyphrases
- back propagation
- approximate inference
- generative model
- deep belief networks
- conditional random fields
- graphical models
- probabilistic model
- artificial neural networks
- neural network
- gaussian process
- latent variables
- probabilistic inference
- belief propagation
- exact inference
- parameter estimation
- message passing
- bayesian framework
- mixture model
- multilayer perceptron
- hidden layer
- loopy belief propagation
- learning algorithm
- bayesian networks
- fuzzy logic
- em algorithm
- semi supervised
- prior knowledge
- topic models
- structured prediction
- random variables
- markov networks
- bayesian network inference
- bayesian inference
- unsupervised learning
- deep learning
- belief networks
- latent dirichlet allocation