Authors
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, Yoshua Bengio
Publication date
2007/6/20
Book
Proceedings of the 24th international conference on Machine learning
Pages
473-480
Description
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.
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H Larochelle, D Erhan, A Courville, J Bergstra… - Proceedings of the 24th international conference on …, 2007