  Deep learning typically requires large data sets and much compute power for
each new problem that is learned. Meta-learning can be used to learn a good
prior that facilitates quick learning, thereby relaxing these requirements so
that new tasks can be learned quicker; two popular approaches are MAML and the
meta-learner LSTM. In this work, we compare the two and formally show that the
meta-learner LSTM subsumes MAML. Combining this insight with recent empirical
findings, we construct a new algorithm (dubbed TURTLE) which is simpler than
the meta-learner LSTM yet more expressive than MAML. TURTLE outperforms both
techniques at few-shot sine wave regression and image classification on
miniImageNet and CUB without any additional hyperparameter tuning, at a
computational cost that is comparable with second-order MAML. The key to
TURTLE's success lies in the use of second-order gradients, which also
significantly increases the performance of the meta-learner LSTM by 1-6%
accuracy.
