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Yoshua Bengio (2009), "Learning Deep Architectures for AI", Foundations and Trends® in Machine Learning: Vol. 2: No. 1, pp 1-127. http://dx.doi.org/10.1561/2200000006
Published: 15 Nov 2009
© 2009 Y. Bengio
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level 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 monograph 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.
Learning+Deep+Architectures+for+AI.pdf
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