We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approximate parameter estimation method based on the minimum description length (MDL) principle, which can be seen as maximising a variational lower bound on the log-likelihood, with a feedforward neural network implementing approximate inference. We demonstrate state-of-the-art generative performance on a number of classic data sets: several UCI data sets, MNIST and Atari 2600 games.
@article{arxiv.1310.8499,
title = {Deep AutoRegressive Networks},
author = {Karol Gregor and Ivo Danihelka and Andriy Mnih and Charles Blundell and Daan Wierstra},
journal= {arXiv preprint arXiv:1310.8499},
year = {2014}
}
Comments
Appears in Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014