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Learning Deep Hierarchies of Representations

Learning Deep Hierarchies of Representations

This video was recorded at VideoLectures.NET - Single Lectures Series. Whereas theoretical work suggests that deep architectures might be computationally and statistically more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pre-training of each level of a hierarchically structured model. Several unsupervised criteria and procedures were proposed for this purpose, starting with the Restricted Boltzmann Machine (RBM), which when stacked gives rise to Deep Belief Networks (DBN). Although the partition function of RBMs is intractable, inference is tractable and we review several successful learning algorithms that have been proposed, in particular those using weights that change quickly... Show More

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Antonio Silva Sprock
Antonio Silva Sprock (Teacher (K-12))
4 years ago

Excelent material

Used in course? Yes
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