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: This paper studies the effect of parametric activations in adversarial training. Results show improved robustness against multiple attacks.
strengths_and_weaknesses: Strengths:
- The writing is mostly clear. 
- The paper designs a parametric activation function. 
- Results show improved robustness against multiple attacks by using the designed activation function. 

Weaknesses:
- It is unclear how the models are trained, and if the proposed activation function can improve adversarial training.
- Only some basic gradient-based white-box attacks are considered. I am not sure if there can be an issue of obfuscated gradients [1] and if the robustness still holds against other attacks in [1]. 
- There are many other works on parametric activations in adversarial training, which are not compared (see https://scholar.google.com/scholar?hl=en&q=PARAMETRIC+ACTIVATION++in+adversarial+training). 

[1] Athalye, A., Carlini, N., & Wagner, D. (2018, July). Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In International conference on machine learning (pp. 274-283). PMLR.

suggested_changes: Use parenthetical citation (\citep) instead of in many places of the paper, if the author names are not part of the sentence to be read.
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This paper leverages the property of activation functions to improve the robustness of adversarial training.
strengths_and_weaknesses: Strengths:
1. Exploit the shape of the activations for better robustness.

Weaknesses:
1. The idea is not novel and this work cannot contribute to the community. Using different activations for adversarial training have been explored before.
2. The authors have not described why they want to propose the scheme described in the paper.
3. Lack of comparison. Experiments are wrong and very confusing. The authors have not detailed the setup and compared with other works.
4. The written and organization are very bad.

suggested_changes: See the weaknesses part.
review_title: null

Decision: Invite to present