Central Limit Theorem for Bayesian Neural Network trained with Variational Inference.
Arnaud DescoursTom HuixArnaud GuillinManon MichelÉric MoulinesBoris NectouxPublished in: CoRR (2024)
Keyphrases
- variational inference
- central limit theorem
- posterior distribution
- probability distribution
- neural network
- bayesian inference
- multilayer perceptron
- topic models
- probabilistic model
- gaussian process
- probabilistic graphical models
- mixture model
- latent dirichlet allocation
- variational methods
- exact inference
- back propagation
- bayesian networks
- posterior probability
- random variables
- artificial neural networks
- closed form
- latent variables
- training set
- heavy traffic
- neural network model
- factor graphs
- hyperparameters
- bayesian framework
- markov chain monte carlo
- hidden variables
- exponential family
- graphical models
- approximate inference
- parameter estimation
- prior information
- conditional probabilities
- support vector
- dynamic programming