  Fully automating machine learning pipelines is one of the key challenges of
current artificial intelligence research, since practical machine learning
often requires costly and time-consuming human-powered processes such as model
design, algorithm development, and hyperparameter tuning. In this paper, we
verify that automated architecture search synergizes with the effect of
gradient-based meta learning. We adopt the progressive neural architecture
search \cite{liu:pnas_google:DBLP:journals/corr/abs-1712-00559} to find optimal
architectures for meta-learners. The gradient based meta-learner whose
architecture was automatically found achieved state-of-the-art results on the
5-shot 5-way Mini-ImageNet classification problem with $74.65\%$ accuracy,
which is $11.54\%$ improvement over the result obtained by the first
gradient-based meta-learner called MAML
\cite{finn:maml:DBLP:conf/icml/FinnAL17}. To our best knowledge, this work is
the first successful neural architecture search implementation in the context
of meta learning.
