UFLDL Recommended Readings

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* [http://www.cs.toronto.edu/~hinton/science.pdf]  Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006.   
* [http://www.cs.toronto.edu/~hinton/science.pdf]  Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006.   
** If you want to play with the code, you can also find it at [http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html].  
** If you want to play with the code, you can also find it at [http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html].  
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* [http://www-etud.iro.umontreal.ca/~larocheh/publications/greedy-deep-nets-nips-06.pdf] Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. Greedy Layer-Wise Training of Deep Networks. NIPS 2006  
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* [http://books.nips.cc/papers/files/nips19/NIPS2006_0739.pdf] Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. Greedy Layer-Wise Training of Deep Networks. NIPS 2006  
* [http://www.cs.toronto.edu/~larocheh/publications/icml-2008-denoising-autoencoders.pdf] Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol. Extracting and Composing Robust Features with Denoising Autoencoders. ICML 2008.   
* [http://www.cs.toronto.edu/~larocheh/publications/icml-2008-denoising-autoencoders.pdf] Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol. Extracting and Composing Robust Features with Denoising Autoencoders. ICML 2008.   
** (They have a nice model, but then backwards rationalize it into a probabilistic model.  Ignore the backwards rationalized probabilistic model.) (Someone please clarify eactly which section of the paper this is.)
** (They have a nice model, but then backwards rationalize it into a probabilistic model.  Ignore the backwards rationalized probabilistic model.) (Someone please clarify eactly which section of the paper this is.)

Revision as of 06:34, 18 March 2011

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