# A fast learning algorithm for deep belief nets

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### Cites background from "A fast learning algorithm for deep ..."

...Deep belief networks (DBNs) [16] are hybrid models containing a single undirected layer and several directed layers....

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...An alternative to directed graphical models with latent variables are undirected graphical models with latent variables, such as restricted Boltzmann machines (RBMs) [27, 16], deep Boltzmann machines (DBMs) [26] and their numerous variants....

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^{1}, Geoffrey E. Hinton

^{1}, Alex Krizhevsky

^{1}, Ilya Sutskever

^{1}+1 more•Institutions (1)

27,534 citations

### Cites methods from "A fast learning algorithm for deep ..."

...2 Learning Dropout RBMs Learning algorithms developed for RBMs such as Contrastive Divergence (Hinton et al., 2006) can be directly applied for learning Dropout RBMs....

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##### References

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### "A fast learning algorithm for deep ..." refers methods in this paper

...Using local elastic deformations in a convolutional neural network, Simard, Steinkraus, and Platt (2003) achieve 0.4%, which is slightly better than the 0.63% achieved by the best hand-coded recognition algorithm (Belongie, Malik, & Puzicha, 2002)....

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...Using local elastic deformations in a convolutional neural network, Simard, Steinkraus, and Platt (2003) achieve 0.4%, which is slightly better than the 0.63% achieved by the best hand-coded recognition algorithm (Belongie, Malik, & Puzicha, 2002)....

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