text: This paper studies the adjustment of dropout rates  which is a useful tool to prevent the overfitting of deep neural networks.  The authors derive a generalization error bound in terms of dropout rates.  Based on this, the authors propose a regularization framework to adaptively select dropout rates.  Experimental results are also given to verify the theory. Major comments: (1) The Empirical Rademacher complexity is not defined.  For completeness, it would be better to define it at least in the appendix. (2) I can not follow the inequality (5).  Especially, according to the main text, f^L is a vector-valued function .  Therefore, it is not clear to me the meaning of \sum\sigma_if^L(x_i,w) in (5). (3) I can also not see clearly the third equality in (9).  Note that f^l is a vector-valued function.  It is not clear to me how it is related to a summation over j there. (4) There is a linear dependency on the number of classes in Theorem 3.1.  Is it possible to further improve this dependency? Minor comments:(1) Section 4: 1e-3,1e-4,1e-5 is not consistent with 1e^{-3}, 1e^{-4},1e^{-5} (2) Abstract: there should be a space before "Experiments". (3) It would be better to give more details (e.g., page, section) in citing a book in the proof of Theorem 3.1 Summary: The mathematical analysis in the present version is not rigorous.  The authors should improve the mathematical analysis.