Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation.
Manuel HaußmannFred A. HamprechtMelih KandemirPublished in: UAI (2019)
Keyphrases
- back propagation
- variational inference
- neural network
- bayesian inference
- posterior distribution
- markov chain monte carlo
- artificial neural networks
- feed forward
- gaussian process
- probabilistic model
- probabilistic graphical models
- topic models
- variational methods
- multilayer perceptron
- bp algorithm
- mixture model
- backpropagation neural networks
- training algorithm
- hidden layer
- latent dirichlet allocation
- hyperparameters
- feedforward neural networks
- exponential family
- backpropagation algorithm
- neural nets
- random sampling
- closed form
- probability distribution
- learning algorithm
- activation function
- backpropagation neural network
- posterior probability
- latent variables
- parameter estimation
- fuzzy logic
- hidden variables
- gaussian processes
- bayesian framework
- sample size
- graphical models
- multilayer neural network
- bayesian networks
- maximum a posteriori
- expectation maximization
- exact inference
- approximate inference
- knowledge base
- em algorithm
- level set
- model selection
- least squares
- semi supervised
- neural network model