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]:
This paper rederives the fast dropout method which is a deterministic alternative to the originally proposed stochastic dropout regularization for neural networks. Then the authors apply fast dropout to recurrent neural networks (RNN) on two different datasets. 

The main focus of the paper is the derivation of the fast dropout training with almost no link to RNNs until the experiment sections, which makes the paper consists of two disconnected components. The authors could have presented experiments on feedforward NN without affecting the smoothness of the paper at all.

The results are expected. Dropout, on small datasets, does better than baseline systems which doesn’t use any stochastic regularization and is as good as ones that does (Graves 2013). In table 2, it is mentioned that assumptions made in RNN-NADE system is also applicable to FD. The authors are encouraged to add these assumptions to get better results.

The results sections (3.1.2 and 3.2.2) still need more work. There are no analysis or discussion about the achieved results. Analysis of the effects of network structure, dropout rate, and other hyper-parameters are missing. The tables' order is reversed (table 3 is referenced first then table 2 and table 1).

For the results in 3.1.2, please clarify if there is a test data on which you measure NLL or you measure performance on the training data only (there is no training/test division menitoned for the music datasets).