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Learning to Learn with Compound HD Models
2011
Neural Information Processing Systems
We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian models. Specifically we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a Deep Boltzmann Machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training examples, by learning low-level generic features, high-level features that
dblp:conf/nips/SalakhutdinovTT11
fatcat:zgmboicurrcntkx2hih33ylhmm