Few-sample Variational Inference of Bayesian Neural Networks with Arbitrary Nonlinearities.
David J. SchodtPublished in: CoRR (2024)
Keyphrases
- variational inference
- bayesian inference
- neural network
- posterior distribution
- topic models
- probabilistic model
- gaussian process
- mixture model
- latent dirichlet allocation
- probabilistic graphical models
- variational methods
- closed form
- latent variables
- exponential family
- hyperparameters
- exact inference
- posterior probability
- probability distribution
- bayesian framework
- generative model
- parameter estimation
- markov networks
- fuzzy logic
- sample size
- markov chain monte carlo
- unsupervised learning
- factor graphs
- approximate inference
- prior information
- machine learning
- graphical models
- optic flow
- cross validation
- maximum a posteriori