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Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.
All You Need is a Good Functional Prior for Bayesian Deep Learning
This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional prior, which is the distribution of the functions obtained by sampling the parameters from their prior distribution.
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes
The Bayesian treatment of neural networks dictates that a prior distribution is considered over the weight and bias parameters of the network.
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