Venue: ICLR 2023 TinyPapers

Reviews:

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 work proposes a method that combines temporal attention with an adaptive Leaky-Integrate-and-Fire neuron model to improve image decoding in Spiking Neural Networks (SNNs). The proposed method achieves state-of-the-art results in MNIST and CIFAR-10 benchmarks.
strengths_and_weaknesses: ### Strengths:
1. The paper is well-motivated and clear to understand.
2. The authors have conducted thorough experimentation on MNIST and CIFAR-10 to justify the correctness of their approach. I especially like the ablation studies in Table 5 which help in further understanding of the proposed design choices.

### Weakness:
1. Regarding reproducibility, the authors have not provided any code nor they have expressed any intention of open sourcing the code in the submission. I request the authors to clarify on this. 

suggested_changes: - I recommend an acceptance of this submission with a High Potential rating. While the method does borrow design choices from prior methods (Kamata et al. 2022 and Fang et al. 2021), the overall method performs very well on the selected benchmarks.

- I would also ask the authors to discuss more on Table 5. Also a small paragraph on how tau and Vth are tuned would help in further understanding of the approach. 

- **Change** : In Table 2, it looks like ANN has the lowest FDD score. Therefore, its value should be in bold font and not your method's FDD score.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This submission proposes an spiking variational autoencoder built out of adaptive LIF neurons, and shows that it outperforms previously-proposed spiking architectures for image generation.
strengths_and_weaknesses: I assess the paper's strengths and weaknesses with respect to each of the reviewing criteria below: 

- **Clarity**: I found the description of the method and of the results fairly easy to follow. 

- **Correctness**: The experimental evaluation appears solid, and the claimed improvements on past work seem to be adequately demonstrated.

- **Reproducibility**: The architecture and experimental setup are described in sufficient detail to allow reproduction. It could be useful to release code upon acceptance.

- **Follows basic requirements**: The paper fits within the page limit, and is adequately anonymized. 
suggested_changes: 1. As mentioned above, please consider releasing code to allow easy reproduction of your results. 

2. A small formatting suggestion to improve readability: use \citep or \parencite rather than \citet or \textcite when appropriate. For instance, in the first sentence, set "Roy et al. (2019)" as "(Roy et al. 2019)". 

3. There are a few places where I think some editing could improve the clarity of the manuscript. For example, what do the authors mean when they write "LIF is expressed more iteratively?" Based on referring to Appendix 1, they appear to use "iterative" to refer to the first-order discretization of the LIF dynamics; this could be stated more clearly. 

4. The diagram in Figure 1 is so small as to be nearly illegible. I'd suggest that the authors make it larger, and, if necessitated by length constraints, move it to the Appendix.

5. The non-spiking ANN baseline in Tables 1-4 does not seem to be clearly described. What activation function is used? How far is this network from the the state-of-the-art?
review_title: null

Decision: Invite to present