UFLDL Recommended Readings

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* Natural Image Statistics book, Hyvarinen et al.  
* Natural Image Statistics book, Hyvarinen et al.  
* Olshausen and Field Sparse Coding paper (1996)  
* Olshausen and Field Sparse Coding paper (1996)  
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* Learning Deep Architectures for AI. (Broad landscape description of the field, but technical details there are hard to follow so ignore that.)
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* [http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf] Yoshua Bengio. Learning Deep Architectures for AI. FTML 2009. (Broad landscape description of the field, but technical details there are hard to follow so ignore that.)
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* Rajat Raina's Self-Taught Learning paper from ICML 2009. (2008?)
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* [http://www.cs.stanford.edu/~ang/papers/icml07-selftaughtlearning.pdf]  Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer and Andrew Y. Ng. Self-taught learning: Transfer learning from unlabeled data. ICML 2007
Autoencoders:  
Autoencoders:  
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* Greedy layerwise training of autoencoders
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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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* Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. Why Does Unsupervised Pre-training Help Deep Learning? Journal of Machine Learning Research, 11(Feb):625−660, 2010   
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* [http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf] Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. Why Does Unsupervised Pre-training Help Deep Learning? JMLR 2010   
* Larochelle, Erhan, Courville, Bergstra, BBengio, 2007.  (Someone read this and let us know if this is worth keeping,.)  
* Larochelle, Erhan, Courville, Bergstra, BBengio, 2007.  (Someone read this and let us know if this is worth keeping,.)  
* [http://www.cs.toronto.edu/~hinton/science.pdf] [http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html] 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] [http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html] Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006  

Revision as of 01:47, 1 March 2011

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