Neural architecture search (NAS) advances beyond the state-of-the-art in various computer vision tasks by automating the designs of deep neural networks. In this talk, we aim to address three important questions in NAS: (1) How to measure the correlation between architectures and their performances? (2) How to evaluate the correlation between different architectures? (3) How to learn these correlations with a small number of samples? To this end, we first model these correlations from a Bayesian perspective. Specifically, by introducing a novel Gaussian Process based NAS (GP-NAS) method, the correlations are modeled by the kernel function and mean function. The kernel function is also learnable to enable adaptive modeling for complex correlations in different search spaces. GP-NAS enables direct performance prediction of any architecture in different scenarios and may obtain efficient networks for different deployment platforms. GP-NAS won the Winner prize in all three tracks of the AIM 2020 Real Image Super-Resolution Challenge, the Winner prize in OVIC Image Track of 2020 Low-Power Computer Vision Challenge.