Reviews: Quantized Random Projections and Non-Linear Estimation of Cosine Similarity

I actually liked the paper quite a bit, but I do have at least a few concerns. First of all, it is very important in establishing the result that the approach to estimating the inner product between the observations is *not* simply taking the inner product between the observations, but by computing the MLE of the inner product. I see how the authors do this in the Gaussian case, but for this to be relevant in practice (on truly high-dimensional data) it seems that it would be important for this to be possible with other kinds of randomized embeddings (such as the results of the Fast Johnson-Lindenstrauss Transform). Some discussion about whether or not the techniques presented would be relevant in such a setting would be welcome. My other main concern is the manner in which the authors have brushed aside the issue of normalization.