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: GCN-based methods have shown promising results in spatial-temporal prediction, although they have as their weak point the fact that they assign equal weights to neighboring nodes. On the other hand, however, GAT assigns equal attention to neighbors without considering channel-specific information. Motivated by the aforementioned challenge, the authors propose MGAT, an improved GAT that efficiently computes the weights within different channel-specific representations.
strengths_and_weaknesses: The contribution of the paper is clearly presented, but needs some fixes regarding English and grammatical aspects. The results presented are on known benchmarks and show improvements.
suggested_changes: It would have been nice to see more theoretical explanations. Finally, the paper could provide information about the limitation of the proposed approach.
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: The authors introduce multi-channel graph attention for graph convolutional models that takes advantage of varying importance of different channels pertaining to the learning task. The idea is formalized and experimental results are presented to show that the proposed idea is effective.
strengths_and_weaknesses: Strengths:
The paper clearly states the idea and formalizes it. Previous work is cited appropriately. Empirical results show that the proposed method improves state-of-the-art models.
Weakness:
The paper could mention if there is any research that shows channels have important information. That is the motivation for this work so it would make sense to include any prior work in that area.
suggested_changes: 1. Is the module plug-and-play? Does the given ST-GCN need to satisfy any criteria so that it can be mated with the proposed MGAT module?
2. What other domains could this technique be useful in? Traffic data is shown here. Image classification, maybe? Does it have to spatial temporal only?
3. A slightly basic question: In the given example of traffic flow/speed data, what does a channel represent? In case of image domain, RGB info could be considered as channels. What about in ST data?
4. Minor suggestion: In the footnote of Table 1, use the the character "`" in latex to print an opening single quote.
review_title: null

Meta-review: metareview: The paper proposes a multi-channel graph attention network that efficiently computes the weights within different channel-specific representations.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present (notable)