Deep Belief Networks资料汇总
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设做的是DBNs的相关研究，翻过一些资料，在此做个汇总。
可以通过谷歌学术搜索来下载这些论文。
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Arel, I., Rose, D. C. and K?arnowski, T. P. Deep machine learning - a new frontier in artificial intelligence research. Computational Intelligence Magazine, IEEE, vol. 5, pp. 13-18, 2010.
深度学习的介绍性文章，可做入门材料。
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Bengio, Y. Learning deep architecture for AI. Foundations and Trends in Machine Learning, vol. 2, pp: 1-127, 2009.
深度学习的经典论文，集大成者。可以当作深度学习的学习材料。
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Hinton, G. E. Learning multiple layers of representation. Trends in Cognitive Sciences, vol. 11, pp. 428-434, 2007.
不需要太多数学知识即可掌握DBNs的关键算法。这篇论文语言浅白，篇幅短小，适合初学者理解DBNs。
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Hinton, G. E. To recognize shapes, first learn to generate images. Technical Report UTML TR 2006-003, University of Toronto, 2006.
多伦多大学的内部讲义。推荐阅读。
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Hinton, G. E., Osindero, S. and Teh, Y. W. A fast learning algorithm for deep belief nets. Neural Computation, vol 18, pp. 1527-1554, 2006.
DBNs的开山之作，意义非凡，一定要好好看几遍。在这篇论文中，作者详细阐述了DBNs的方方面面，论证了其和一组层叠的RBMs的等价性，然后引出DBNs的学习算法。
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Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, vol. 313, no. 5786, pp. 504–507, 2006.
Science上的大作。这篇论文可是算作一个里程碑，它标志着深度学习总算有了高效的可行的算法。
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Hinton, G. E. A practical guide to training restricted boltzmann machines. Technical Report UTML TR 2010-003, University of Toronto, 2010.
一份训练RBM的最佳实践。
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Erhan, D., Manzagol, P. A., Bengio, Y., Bengio, S. and Vincent, P. The difficulty of training deep architectures and the effect of unsupervised pretraining. In The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 153–160, 2009.
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Erhan, D., Courville, A., Bengio, Y. and Vincent, P. Why Does Unsupervised Pre-training Help Deep Learning? In the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Chia Laguna Resort, Sardinia, Italy, 2010.
阐述了非监督预训练的作用。这两篇可以结合起来一起看。
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这篇博客给出的材料更加全面，作者来自复旦大学，现似乎是在Yahoo Labs北京研究院工作。
http://demonstrate.ycool.com/post.3006074.html
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