Learning to Learn with Compound HD Models

author: Ruslan Salakhutdinov, Department of Statistical Sciences, University of Toronto
published: Sept. 6, 2012,   recorded: December 2011,   views: 3794
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Description

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 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.

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Download slides icon Download slides: nips2011_salakhutdinov_hdmodels_01.pdf (2.1 MB)

Download article icon Download article: nips2011_1163.pdf (630.6 KB)


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