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IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1958-71. doi: 10.1109/TPAMI.2012.269.

Learning with hierarchical-deep models.

Author information

1
Department of Statistics and Computer Science, University of Toronto, Toronto, ON M5S 3G3, Canada. rsalakhu@utstat.toronto.edu

Abstract

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) 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 example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

PMID:
23787346
DOI:
10.1109/TPAMI.2012.269
[Indexed for MEDLINE]

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