The discovery of neural architectures from scratch is the long-standing goal
of Neural Architecture Search (NAS). Searching over a wide spectrum of neural
architectures can facilitate the discovery of previously unconsidered but
well-performing architectures. In this work, we take a large step towards
discovering neural architectures from scratch by expressing architectures
algebraically. This algebraic view leads to a more general method for designing
search spaces, which allows us to compactly represent search spaces that are
100s of orders of magnitude larger than common spaces from the literature.
Further, we propose a Bayesian Optimization strategy to efficiently search over
such huge spaces, and demonstrate empirically that both our search space design
and our search strategy can be superior to existing baselines. We open source
our algebraic NAS approach and provide APIs for PyTorch and TensorFlow.

通过代数视角来表达网络结构，我们提出了一种更具有广泛性的方法来设计搜索空间，并提出了一种 Bayesian Optimization 策略来高效地在巨大的搜索空间中搜索，实验证明我们的搜索空间设计和搜索策略可以优于现有基准。