Auto-Meta: Automated Gradient Based Meta Learner Search
  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.
