Title: Modular meta-learning,
Abstract: Many prediction problems, such as those that arise in the context of
robotics, have a simplifying underlying structure that could accelerate
learning. In this paper, we present a strategy for learning a set of neural
network modules that can be combined in different ways. We train different
modular structures on a set of related tasks and generalize to new tasks by
composing the learned modules in new ways. We show this improves performance in
two robotics-related problems.