Title: Deep Prior,
Abstract: The recent literature on deep learning offers new tools to learn a rich
probability distribution over high dimensional data such as images or sounds.
In this work we investigate the possibility of learning the prior distribution
over neural network parameters using such tools. Our resulting variational
Bayes algorithm generalizes well to new tasks, even when very few training
examples are provided. Furthermore, this learned prior allows the model to
extrapolate correctly far from a given task's training data on a meta-dataset
of periodic signals.