Reviews: Learning Compressed Transforms with Low Displacement Rank

I find presented results interesting and valuable, however it is unclear how significant for the community they really are. Authors seem to view the approach as a way to decrease overparametrisation of the networks with minimal effect on accuracy. However, there are dozens of techniques which try to address this very issue, which are not really compared against. Instead, authors choose to focus on comparing to other, very similar approaches of reparametrising neural network layers with LDRs. Pros: - Clear message - Visible extension of previous work/results - Showing both empirically and theoretically how proposed method improves upon baselines - Providing implementation of the method, thus increasing reproducability Cons: - all experiments are relatively low-scale, and the only benefits over not constraining the structure is obtained for MNIST-noise (90 vs 93.5%) and CIFAR-10 (65 vs 66%) which are not very significant differences at this level of accuracy for these problems.