  Compared to humans, machine learning models generally require significantly
more training examples and fail to extrapolate from experience to solve
previously unseen challenges. To help close this performance gap, we augment
single-task neural networks with a meta-recognition model which learns a
succinct model code via its autoencoder structure, using just a few informative
examples. The model code is then employed by a meta-generative model to
construct parameters for the task-specific model. We demonstrate that for
previously unseen tasks, without additional training, this Meta-Learning
Autoencoder (MeLA) framework can build models that closely match the true
underlying models, with loss significantly lower than given by fine-tuned
baseline networks, and performance that compares favorably with
state-of-the-art meta-learning algorithms. MeLA also adds the ability to
identify influential training examples and predict which additional data will
be most valuable to acquire to improve model prediction.
