Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study.
Tom HuixSzymon MajewskiAlain DurmusEric MoulinesAnna KorbaPublished in: CoRR (2022)
Keyphrases
- variational inference
- theoretical and empirical study
- neural network
- bayesian inference
- posterior distribution
- topic models
- probabilistic graphical models
- mixture model
- swarm intelligence
- probabilistic model
- gaussian process
- variational methods
- latent dirichlet allocation
- closed form
- artificial neural networks
- exact inference
- bayesian framework
- exponential family
- probability distribution
- multilayer perceptron
- parameter estimation
- approximate inference
- graphical models
- markov chain monte carlo
- fuzzy logic
- latent variables
- hyperparameters
- genetic algorithm
- bayesian networks
- factor graphs
- probabilistic inference
- generative model
- posterior probability
- artificial intelligence
- maximum likelihood
- maximum a posteriori
- ant colony optimization
- markov networks
- hidden variables
- particle swarm optimization
- model selection