Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors present the relationship between sleep patterns (monophasic sleep and sleep deprivation) and the learning performance of the Forward-Forward algorithm, a new (2022) learning procedure that consists of two forward passes, one with positive data and one with negative data (in the following as sleep). There seems to be a correlation between negative data (both in quantity and quality) and learning performance.
strengths_and_weaknesses: ### Strengths:
1. The paper is well-written and easy to follow.
2. The claims and conclusions are justified by the findings
suggested_changes: The authors provide interesting findings between sleeping patterns and learning performance, seen in Tables 1 and 2. Specifically, it can be seen that using masks as negative data offer a significant advantage concerning model accuracy. 
For future research, it would interesting to investigate different types of masks on more complicated datasets (a starting point could be CIFAR-10, like in the Forward-Forward paper).
review_title: null

Decision: Invite to revise