Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Impact (HI): a submission which meets the reviewing criteria and is predicted to make an impact on the field
review_content: summary_of_contributions: The paper uses Incremental PCA as a methodology to apply to the Continual Learning task.
strengths_and_weaknesses: 
Pretty solid paper. Theoretical results, time and space complexity analysis, and good details in the appendix buttress the paper. It almost feels like a paper in itself. 
Explain more about the ART framework. 
suggested_changes: 
Explain more about the ART framework.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This paper aims to solve the problem of Catastrophic forgetting using Adaptive Resonance Theory and Incremental PCA. The results are benchmarked on Split MNIST and Split Fashion MNIST. According to the results shared in the paper their method seems to perform on par with the Static PCA. 
strengths_and_weaknesses: ### Strengths : 
-  I think the paper was very well written especially the motivation behind their method was clearly explained as well as the experimental details. The notation seems to be consistent which is excellent.
- The proofs of the proposition, and the time complexity of the algorithm is certainly nice to have and adds more credibility to the paper.
-  The inclusion of algorithm and its simplicity suggests it should be easy to replicate the results, although I have not tried to do that.

### Weakness : 
- I think there is a lack of discussion section/future works section that might be helpful to the readers of the paper to develop better intuition in what more could be done. It would certainly make the paper a more exciting read in my opinion.
-  There should have been at least 1 more continual method to benchmark against so a reader can fully understand the extent of the method. 

suggested_changes: -  As mentioned above, having one more baseline to benchmark against would be very nice. 
-  I would certainly like it if there was a future directions/discussion section to see what the authors further plan to go with their method.

Overall I do think this paper is very well written and fun to read, so super job on that to authors !
review_title: null

Meta-review: metareview: The paper is well-written, compact, appears to be original, and proposes a method that benefits from simplicity and fast run-times. Both reviewers agree that the paper is clear, correct, and reproducible. Reviewer 7Yc5 points out that a non-ART baseline would be important to increase a reader's clarity on how to contextualize the results table, which I agree would improve the paper.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present