Neural architecture search automates the design of neural network
architectures usually by exploring a large and thus complex architecture search
space. To advance the architecture search, we present a graph diffusion-based
NAS approach that uses discrete conditional graph diffusion processes to
generate high-performing neural network architectures. We then propose a
multi-conditioned classifier-free guidance approach applied to graph diffusion
networks to jointly impose constraints such as high accuracy and low hardware
latency. Unlike the related work, our method is completely differentiable and
requires only a single model training. In our evaluations, we show promising
results on six standard benchmarks, yielding novel and unique architectures at
a fast speed, i.e. less than 0.2 seconds per architecture. Furthermore, we
demonstrate the generalisability and efficiency of our method through
experiments on ImageNet dataset.

使用基于图扩散的神经网络架构搜索方法，通过离散条件图扩散过程生成高性能神经网络架构，并应用多条件无分类器引导方法来共同施加高准确性等约束。