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One-shot Neural Architecture Search (NAS) aims to minimize the computationalexpense of discovering state-of-the-art models. However, in the past yearattention has been drawn to the comparable performance of naive random searchacross the same search spaces used by leading NAS algorithms. To address this,we explore the effects of drastically relaxing the NAS search space, and wepresent Bonsai-Net, an efficient one-shot NAS method to explore our relaxedsearch space. Bonsai-Net is built around a modified differential pruner and canconsistently discover state-of-the-art architectures that are significantlybetter than random search with fewer parameters than other state-of-the-artmethods. Additionally, Bonsai-Net performs simultaneous model search andtraining, dramatically reducing the total time it takes to generatefully-trained models from scratch.

Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners