Training Invariant Support Vector Machines
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Training products of experts by minimizing contrastive divergence
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A fast learning algorithm for deep belief nets
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Learning methods for generic object recognition with invariance to pose and lighting
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Classification using discriminative restricted Boltzmann machines
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Training restricted Boltzmann machines using approximations to the likelihood gradient
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Deep, narrow sigmoid belief networks are universal approximators
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Recently, several learning algorithms relying on models with deep architectures have been proposed. Though they have demonstrated impressive performance, to date, they have only been evaluated on relatively simple problems such as digit recognition in a controlled environment, for which many machine learning algorithms already report reasonable results. Here, we present a series of experiments which indicate that these models show promise in solving harder learning problems that exhibit many factors of variation. These models are compared with well-established algorithms such as Support Vector Machines and single hidden-layer feed-forward neural networks.