Arel, I., Rose, D. C. and Karnowski, 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。
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。
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 。
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。
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。