Venue: ML_Reproducibility_Challenge 2020

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The report is well written and has a concise explanation. Additionally, the authors provide a great summary at the beginning. I would like to know if the numbers from the TensorFlow implementation (authors' paper) match the results reported in the main paper. It would interesting to perform a hyperparameter search and provide code and docs so it would be reviewed before accepting the paper. After the authors of the report make some claims and report an improvement for their own implementation. I suggest accepting the report
review_title: review

final_score: rating: 7: Good paper, accept
review_content: As a first remark, the report seems to repeat content. This is probably not the author's fault, I noticed the same problem with other reports. That is probably a problem with the instruction template. This is something for the organizers to take into consideration.

General remarks
The report follows the prescribed format. The authors reproduced the experiments in pytorch. The original implementation was in tensorflow. The author found errors in the implementation and a lot of gaps in the parameter tuning. In some cases, they get results opposite from the claims of the paper. The biggest problem of the paper they evaluated was the fact that the text and code were not in agreement. In fact, the code was convoluted and it was difficult to actually get the knowledge out of it. 
The report is well organized and there is a clear correspondence between the code and the issues the report addresses

Problems of the report
The report has a lot of references to nowhere, this is probably a problem with their latex referencing system. There are references to sections, tabs, and an appendix. While this is a technicality that can easily be resolved for the tables and sections, I couldn't locate the appendix. As mentioned in the comments the authors point that the pytorch function for the node convolution in pytorch is different from the one used in tensorflow. The original paper authors attribute the differences to that. In my opinion, this difference can not be responsible for the discrepancies. 
review_title: The report reveals a lot of dark spots of the original paper

Meta-review: Selected for ReScience-C Journal Publication.

Good reviews, strong reproducibility report, provides code reimplementation from scratch which is a strong contribution. The authors have responded to reviewer comments and have updated their paper accordingly.
Decision: Accept