Review for NeurIPS paper: Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?

Weaknesses: Similar to this work, many recent NAS approaches perform architecture search in the continuous representation space, and gradient descent (GD) is one of the most commonly used approaches for architecture search in the continuous space. However, this work only considers RL and BO as the search algorithms. I am curious to know how competitive GD is compared to RL and BO. In Figure 4, I am not sure why the right plot has more blank space compared to the left plot. An explanation is needed to help the readers understand the insights behind those plots.