  In this work, we focus on an analogical reasoning task that contains rich
compositional structures, Raven's Progressive Matrices (RPM). To discover
compositional structures of the data, we propose the Scattering Compositional
Learner (SCL), an architecture that composes neural networks in a sequence. Our
SCL achieves state-of-the-art performance on two RPM datasets, with a 48.7%
relative improvement on Balanced-RAVEN and 26.4% on PGM over the previous
state-of-the-art. We additionally show that our model discovers compositional
representations of objects' attributes (e.g., shape color, size), and their
relationships (e.g., progression, union). We also find that the compositional
representation makes the SCL significantly more robust to test-time domain
shifts and greatly improves zero-shot generalization to previously unseen
analogies.
