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Learning Deep Architectures for AI
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BibTeX
@MISC{Bengio_learningdeep,
author = {Yoshua Bengio},
title = { Learning Deep Architectures for AI},
year = {}
}
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Abstract
Theoretical results suggest that in order to learn the kind of complicated functions that can represent highlevel abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Keyphrases
deep architecture learning deep architecture deep belief network parameter space non-linear operation ai-level task many hidden layer difficult task multiple level single-layer model restricted boltzmann machine neural net notable success theoretical result complicated function highlevel abstraction building block certain area
