Kang-Moon Park

Neural Architecture Search (NAS) has been widely applied across various fields, revealing intriguing patterns in the resulting optimized topologies. In this paper, we compare the topologies generated by NAS across two different experiments: linguistic grammaticality judgment and the MNIST task. Our analysis reveals a distinctive fork-like structure that consistently emerges in both experiments. Interestingly, this structure is highly effective despite not being typically designed by human experts. The emergence of this fork-like structure sheds new light on the potential of NAS to provide alternative designs that go beyond incremental improvements. Our paper offers a fresh perspective on automated architecture design, highlighting the potential of NAS to enable innovative approaches that can be applied across multiple domains.