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Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks.
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Bengio, Yoshua · 1935
Earlier work this paper cites.
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Bengio, Yoshua et al · 2009
Earlier work this paper cites.
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Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
Earlier work this paper cites.
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Kingma, D. P and Welling, M · 2013
Earlier work this paper cites.
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Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
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Jimenez Rezende, D., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
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Kingma, Diederik and Ba, Jimmy · 2014
Earlier work this paper cites.
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Zeiler, Matthew D and Fergus, Rob · 2014
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E. L., Chintala, S., Fergus, R., et al · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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