Weakness: 1. Although these tensor networks can be used to represent PMF of discrete variables. How these results are useful to machine learning algorithms or analyze the algorithm is not clear. Hence, the significance of this paper is poor. 2. The two experiments are all based on very small dataset either generated or realistic data. The evaluation is performed on KL-divergence or NLL, which only show how good the model can fit the data, rather than generalization performance. How these results are useful for machine learning? In addition, MPS, BM, LPS are quite similar in the structure. There are many well known tensor models, CP, Tucker, Hirachical Tucker are not compared. There are also more complicated models like MERA, PEPS. I have read the authors' rebuttal, they addressed some of questions well. But the generalization is not considered, thus it becomes a standard non-negative tensor factorization problem on the PMF data. Hence, I will remain the original score.
1. Although these tensor networks can be used to represent PMF of discrete variables. How these results are useful to machine learning algorithms or analyze the algorithm is not clear. Hence, the significance of this paper is poor.