Large-scale deep unsupervised learning using graphics processors
Computer Science Department, Stanford University, Stanford CA 94305 USA
In ICML ’09: Proceedings of the 26th Annual International Conference on Machine Learning (2009), pp. 873-880.
@conference{raina2009large,
title={Large-scale deep unsupervised learning using graphics processors},
author={Raina, R. and Madhavan, A. and Ng, A.Y.},
booktitle={Proceedings of the 26th Annual International Conference on Machine Learning},
pages={873–880},
year={2009},
organization={ACM}
}
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free parameters. We consider two well-known unsupervised learning models, deep belief networks (DBNs) and sparse coding, that have recently been applied to a flurry of machine learning applications (Hinton & Salakhutdinov, 2006; Raina et al., 2007). Unfortunately, current learning algorithms for both models are too slow for large-scale applications, forcing researchers to focus on smaller-scale models, or to use fewer training examples.
November 21, 2010 by hgpu
