Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The main claim of the paper is that a previously published method for crime prediction can be augmented to produce not only a single spatiotemporal prediction, but a probability distribution over all possible spatiotemporal values. This is achieved by adding a VAE to STResnet.
strengths_and_weaknesses: The major weakness of this paper is its lack of clarity. In particular, details on the problem and the solution are sparse and/or confusing throughout the paper. Furthermore, although the main contribution of the paper is obtaining a probability distribution over the predictions as compared to prior solutions, there is no discussion or showcasing of this in the results. F1 scores are the only evaluation metric presented which do not surpass prior work substantially, making it difficult to judge the merits and contribution of the current work.

Since the subject matter of the of the paper is crime prediction, any ethical implications of the shortcomings of the model are important to consider. There doesn't seem to be any discussion of this issue in the paper.
suggested_changes: The introduction must be expanded to frame the problem and motivate the solution better. The Figures are a waste of space if they do not have proper captioning to aid in understanding the approach.

There is no high-level description of the problem or the solution. For example, although it is mentioned that past crime data are used as inputs to the model, it is unclear what aspects of the data the Resnet/VAE architecture takes advantage of to predict location and timing of crime.

The descriptions of the approach are also very confusing. The elements of the solution must be clearly described. It must be explained why things are done and how they aid in achieving a better solution. For example, what is the slicing operation? What is the "target"? What are the four "fragments": instant, closeness, period, and trend? Why are they useful?

For evaluation, a comparison with STResnet is appropriate as this work builds directly on it. However, using vanilla GANs for comparisons is not informative. Why not use a VAE for this? This would make it easier to directly compare individual components (STResnet and VAE) to the combination of the two (STResnetVAE) for a fair assessment of whether or not the components, when combined, have synergistic success.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: Authors propose an approach combining existing STResnet and VAE for crime prediction. The proposed approach shows promising results in long distance prediction and on par performance in short distance prediction with existing methods.
strengths_and_weaknesses: * Strengths
    * The proposed approach is simple and easy to implement.
    * The proposed approach shows promising results in long distance prediction and on par performance in short distance prediction with existing methods.
    * Comparisons with GAN and STResnet are provided.
* Weaknesses
    * An extensive ablation study is not provided.
suggested_changes: I think the proposed approach is simple and easy to implement, an ablation study will justify the effectiveness of the proposed approach. Still, I think the proposed approach is a good starting point for future work. I recommend the paper for discussion at ICLR Tiny Papers.
review_title: null

Decision: Invite to archive