Review for NeurIPS paper: Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Additional Feedback: Post-response update: The author response adressed my concerns very well, and the paper is good enough to be accepted, despite the lacking novelty. I am increasing my score to 7. ---- The paper proposes a new more general formalism to handle output constraints in BNNs. The space of constrained neural networks is already crowded, and while section 2 does make a good overview of the differences, it would greatly improve the paper to also define mathematically the differences in competing constraining methods and their scopes. Overall I had hard time understanding the contraint definitions (see below for minor comments). The constraint formalism needs to be explicated better. I could not follow the math anymore.