Autor:	Lacoste, Alexandre, Boquet, Thomas, Rostamzadeh, Negar, Oreshkin, Boris, Chung, Wonchang, Krueger, David
Rok vydání:	2017
Předmět:	Statistics - Machine Learning Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Workshop paper, Accepted at Bayesian Deep Learning workshop, NIPS 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.05016 Zobrazit plný text záznamu View this record from Arxiv