Reviews: Inherent Weight Normalization in Stochastic Neural Networks

Related work is well cited across these fields, and the approach is unique in this reviewer's knowledge. Quality The quality of this work is adequate, though there are a couple of simple errors in the text (misspelling in Figure 1, missing sections in the supplementary material, lack of explanation of some abbreviations such as W_3 and S2M). Overall, the text and derivation is done with high quality, and the tricks used in the derivation are called out to adequately describe the steps to the reader. The conclusions stand on their own, and quality of insight is needed to bridge stochastic neural networks, multiplicative weights, and weight normalization. The work could use more difficult datasets, though, to emphasize these results.