Paper:
ADAPTIVE DROPOUT WITH RADEMACHER COMPLEX- ITY REGULARIZATION
We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. The state-of-the-art deep learning algorithms impose dropout strategy to prevent feature co-adaptation. However, choosing the dropout rates remains an art of heuristics or relies on empirical grid-search over some hyperparameter space. In this work, we show the network Rademacher complexity is bounded by a function related to the dropout rate vectors and the weight coefficient matrices. Subsequently, we impose this bound as a regularizer and provide a theoretical justified way to trade-off between model complexity and representation power. Therefore, the dropout rates and the empirical loss are unified into the same objective function, which is then optimized using the block coordinate descent algorithm. We discover that the adaptively adjusted dropout rates converge to some interesting distributions that reveal meaningful patterns. Experiments on the task of image and document classification also show our method achieves better performance compared to the state-of-the-art dropout algorithms.

Review:
==Main comments
The authors connect dropout parameters to a bound of the Rademacher complexity (Rad) of the network. While it is great to see deep learning techniques inspired by learning theory, I think the paper makes too many leaps and the Rad story is ultimately unconvincing.  Perhaps it is better to start with the resulting regularizer, and the interesting direct optimization of dropout parameters. In its current form, the following leaps problematic and were not addressed in the paper:
1) Why is is adding Rad as a regularizer reasonable? Rad is usually hard to compute, and most useful for bounding the generalization error. It would be interesting if it also turns out to be a good regularizer, but the authors do not say why nor cite anything. Like the VC dimension, Rad itself depends on the model class, and cannot be directly optimized. Even if you can somehow optimize over the model class, these quantities give very loose bounds, and do not equal to generalization error. For example, I feel even just adding the actual generalization error bound is more natural. Would it make sense to just add Rad to the objective in this way for a linear model?
2) Why is it reasonable to go from a regularizer based on RC to a loose bound of Rad? The actual resulting regularizer turns out to be a weight penalty but this seems to be a rather loose bound that might not have too much to do with Rad anymore. There should be some analysis on how loose this bound is, and if this looseness matter at all. 
The empirical results themselves seem reasonable, but the results are not actually better than simpler methods in the corresponding tasks, the interpretation is less confident. Afterall, it seems that the proposed method had several parameters that were turned, where the analogous parameters are not present in the competing methods. And the per unit dropout rates are themselves additional parameters, but are they actually good use of parameters?
==Minor comments
The optimization is perhaps also not quite right, since this requires taking the gradient of the dropout parameter in the original objective. While the authors point out that one can use the mean, but that is more problematic for the gradient than for normal forward predictions. The gradient used for regular learning is not based on the mean prediction, but rather the samples.
tiny columns surrounding figures are ugly and hard to read