Abstract

Abstract We have been studying the learning of compositional hierarchies in predictive models, an area we feel is significantly underrepresented in machine learning. The aim in learning such models is to scale up automatically from fine-grained to coarser representations by identifying frequently occurring repeated patterns, while retaining the ability to make predictions based on the statistica] regularities exhibited by these patterns. Our hierarchical learning begins with data