  How can deep learning systems flexibly reuse their knowledge? Toward this
goal, we propose a new class of challenges, and a class of architectures that
can solve them. The challenges are meta-mappings, which involve systematically
transforming task behaviors to adapt to new tasks zero-shot. The key to
achieving these challenges is representing the task being performed in such a
way that this task representation is itself transformable. We therefore draw
inspiration from functional programming and recent work in meta-learning to
propose a class of Homoiconic Meta-Mapping (HoMM) approaches that represent
data points and tasks in a shared latent space, and learn to infer
transformations of that space. HoMM approaches can be applied to any type of
machine learning task. We demonstrate the utility of this perspective by
exhibiting zero-shot remapping of behavior to adapt to new tasks.
