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 have just uploaded a new version of the paper to arxiv, which will be available at Tue, 18 Feb 2014 01:00:00 GMT.

We address various concerns of the reviewers. Here is a break down of the improvements:

- added figures and text showing the behaviour of fast dropout units (especially delta_k) in various situations,
- added an analysis of the evolution of the recurrent weight matrix during training, which supports our theoretical findings,
- calculation in section 2.2.2 'sampling' corrected,
- added hyper parameters used for the experiments in the appendix,
- included results from [1] for completeness,
- numerous typos and errors fixed.

We want to thank the anonymous reviewers, since we believe their comments to have improved the work.


[1] Pascanu, Razvan, et al. 'How to Construct Deep Recurrent Neural Networks.'arXiv preprint arXiv:1312.6026 (2013).
APA