How can techniques drawn from machine learning be applied to the learning of structured, compositional representations? In this work, we adopt functional programs as our representation, and cast the problem of learning symbolic representations as a symbolic analog of dimensionality reduction. By placing program synthesis within a probabilistic machine learning framework, we are able to model the learning of some English inflectional morphology and solve a set of synthetic regression problems.
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