To identify general principles, researchers are increasingly interested insurveying largecollections ofnetworks that are trained on, or biologically adapted to,similar tasks.

W e will release all the source code and data . Meanwhile, please find our detailed responses below. The key idea of Shi et al. is predicting DARTS search space and will include the results in the final paper. We ran ablation studies based on the comments. LatBench with FP32/FP16/INT8 on supported platforms by November.

We thank the reviewers for their helpful reviews. Please see the details below. See the figure below for the results of Reg. We now provide preliminary results for experiments on the DARTS search space. See the figure below (top right).

Figure 1: Supervised architecture representation learning (top): the supervision signal for representation learning comes from the accuracies of architectures selected by the search strategies.

Graph Bayesian optimization (BO) has shown potential as a powerful and data-efficient tool for neural architecture search (NAS). Most existing graph BO works focus on developing graph surrogates models, i.e., metrics of networks and/or different kernels to quantify the similarity between networks. However, the acquisition optimization, as a discrete optimization task over graph structures, is not well studied due to the complexity of formulating the graph search space and acquisition functions. This paper presents explicit optimization formulations for graph input space including properties such as reachability and shortest paths, which are used later to formulate graph kernels and the acquisition function. We theoretically prove that the proposed encoding is an equivalent representation of the graph space and provide restrictions for the NAS domain with either node or edge labels. Numerical results over several NAS benchmarks show that our method efficiently finds the optimal architecture for most cases, highlighting its efficacy.