Reviews: Improved Dropout for Shallow and Deep Learning

TECHNICAL QUALITY It is discussed in Section 1 (lines 31-33) that features with low/zero variance can be dropped more frequently or even completely. How is this intuition supported by the theoretical analysis in Section 4 (particularly Eq. (8) or (9))? Note that the features are not automatically zero-meaned. The uniform dropout scheme described in line 135 (as a special case of multinomial dropout when all the sampling probabilities are equal) is only similar but not identical to standard dropout (the sampling probabilities for different features are not i.i.d.). It may not be good to use it in the experiments as if it is indeed the standard dropout scheme.