On Fast Dropout and its Applicability to Recurrent Networks
Authors: Justin Bayer, Christian Osendorfer, Sebastian Urban, Nutan Chen, Daniela Korhammer, Patrick van der Smagt
ICLR 2014

Abstract: Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modelling of RNNs, mostly motivated by adressing the problems of the vanishing and exploding gradients.
The control of overfitting has seen considerably less attention.
This paper contributes to that by analyzing fast dropout, a recent regularization method for generalized linear models and neural networks from a back-propagation inspired perspective. We show that fast dropout implements a quadratic form of an adaptive, per-parameter regularizer, which rewards large weights in the light of underfitting, penalizes them for overconfident predictions and vanishes at minima of an unregularized training loss. One consequence of this is the absense of a global weight attractor, which is particularly appealing for RNNs, since the dynamics are not biased towards a certain regime. We positively test the hypothesis that this improves the performance of RNNs on four musical data sets and a natural language processing (NLP) task, on which we achieve state of the art results.


Peer review:
[Review]:
We regret to inform you that we made mistakes in the experimental procedure regarding the Penn Treebank corpus.  Due to these, our submitted results on that benchmark are *invalid*.

The experimental results on the midi tasks are not affected by this. We will shortly submit a version to arxiv from which the relevant sections are removed. Sadly, due to the size of the benchmark we are not be able to rerun the experiments within a reasonable time to be considered for the final decision about the paper.

To allow you to consider the case, we decided to inform you as soon as possible about this issue.  We hope that the chairs and reviewers will still consider our work for the conference.  At the same time, however, we realize that this issue alone may lead to a rejection of the paper.