Deep Prior

Understand

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.