A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference.
Kumar ShridharFelix LaumannMarcus LiwickiPublished in: CoRR (2019)
Keyphrases
- variational inference
- convolutional neural network
- comprehensive guide
- bayesian inference
- posterior distribution
- face detection
- topic models
- probabilistic model
- latent dirichlet allocation
- gaussian process
- mixture model
- variational methods
- probabilistic graphical models
- cutting edge
- social sciences
- closed form
- probability distribution
- latent variables
- easy to follow
- bayesian framework
- exponential family
- parameter estimation
- markov chain monte carlo
- posterior probability
- exact inference
- graphical models
- approximate inference
- maximum a posteriori
- markov networks
- object detection
- factor graphs
- expectation maximization
- bayesian networks
- neural network
- prior information
- hidden variables
- em algorithm
- fuzzy logic
- artificial neural networks
- machine learning