Reviews: Singleshot : a scalable Tucker tensor decomposition

I have read the authors authors rebuttal; I still believe that the experiments are not convincing enough and that a paper claiming to beat the state-of-the-art in scalable tensor decompositions should contain more thorough and clearer experiment setup (larger-scale experiments, fewer ambiguities about experiment setup decisions). The authors demonstrate how the memory blow-up occurring during the Tucker decomposition can be avoided, by formulating the derivations in a way that sub-tensor partitions of the original input tensor can be processed in a sequential manner. The authors provide convergence guarantees of their algorithm to a point where the gradient is equal to zero. Although the approach is sensible and it is backed by theoretical analysis, I find that the experiments are not convincing enough for a work in which improved scalability is regarded as the main advancement. I first list my comments regarding the experiments and provide with additional comments below: - The authors choose to "arbitrarily set the size of the Movielens and Enron tensors to M M 200 and M M 610".