UFLDL Recommended Readings

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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://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.   
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** (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.)
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** (They have a nice model, but then backwards rationalize it into a probabilistic model.  Ignore the backwards rationalized probabilistic model [Section 4].)  
Analyzing deep learning/why does deep learning work:  
Analyzing deep learning/why does deep learning work:  
* [http://www.cs.toronto.edu/~larocheh/publications/deep-nets-icml-07.pdf] H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio. An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation. ICML 2007.
* [http://www.cs.toronto.edu/~larocheh/publications/deep-nets-icml-07.pdf] H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio. An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation. ICML 2007.
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** (Someone read this and let us know if this is worth keeping,.)  
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** (Someone read this and let us know if this is worth keeping,. [Most model related material already covered by other papers, it seems not many impactful conclusions can be made from results, but can serve as reading for reinforcement for deep models])  
* [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   
* [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   
* [http://cs.stanford.edu/~ang/papers/nips09-MeasuringInvariancesDeepNetworks.pdf] Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee and Andrew Y. Ng. Measuring invariances in deep networks. NIPS 2009.  
* [http://cs.stanford.edu/~ang/papers/nips09-MeasuringInvariancesDeepNetworks.pdf] Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee and Andrew Y. Ng. Measuring invariances in deep networks. NIPS 2009.  
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* [http://deeplearning.net/tutorial/rbm.html] Tutorial on RBMs.  
* [http://deeplearning.net/tutorial/rbm.html] Tutorial on RBMs.  
** But ignore the Theano code examples.
** But ignore the Theano code examples.
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** (Someone tell us if this should be moved later.  Useful for understanding some of DL literature, but not needed for many of the later papers?)
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** (Someone tell us if this should be moved later.  Useful for understanding some of DL literature, but not needed for many of the later papers? [Seems ok to leave in, useful introduction if reader had no idea about RBM's, and have to deal with Hinton's 06 Science paper or 3-way RBM's right away])
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* [http://www.iro.umontreal.ca/~lisa/publications2/index.php/attachments/single/57] Yoshua Bengio, Réjean Ducharme, Pascal Vincent and Christian Jauvin, A Neural Probabilistic Language Model. JMLR 2003.
* [http://www.iro.umontreal.ca/~lisa/publications2/index.php/attachments/single/57] Yoshua Bengio, Réjean Ducharme, Pascal Vincent and Christian Jauvin, A Neural Probabilistic Language Model. JMLR 2003.
* [http://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf] R. Collobert and J. Weston. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. ICML 2008.
* [http://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf] R. Collobert and J. Weston. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. ICML 2008.
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* [http://www.socher.org/uploads/Main/SocherPenningtonHuangNgManning_EMNLP2011.pdf] Richard Socher, Jeffrey Pennington, Eric Huang, Andrew Y. Ng, and Christopher D. Manning. Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. EMNLP 2011
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* [http://www.socher.org/uploads/Main/SocherHuangPenningtonNgManning_NIPS2011.pdf] Richard Socher, Eric Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning. Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. NIPS 2011
* [http://www.cs.toronto.edu/~hinton/absps/threenew.pdf] Mnih, A. and Hinton, G. E. Three New Graphical Models for Statistical Language Modelling. ICML 2007
* [http://www.cs.toronto.edu/~hinton/absps/threenew.pdf] Mnih, A. and Hinton, G. E. Three New Graphical Models for Statistical Language Modelling. ICML 2007
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* [http://www.cs.toronto.edu/~ranzato/publications/ranzato_aistats2010.pdf] M. Ranzato, A. Krizhevsky, G. Hinton. Factored 3-Way Restricted Boltzmann Machines for Modeling Natural Images. In AISTATS 2010.
* [http://www.cs.toronto.edu/~ranzato/publications/ranzato_aistats2010.pdf] M. Ranzato, A. Krizhevsky, G. Hinton. Factored 3-Way Restricted Boltzmann Machines for Modeling Natural Images. In AISTATS 2010.
* [http://www.cs.toronto.edu/~ranzato/publications/ranzato_cvpr2010.pdf] M. Ranzato, G. Hinton, Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. CVPR 2010  
* [http://www.cs.toronto.edu/~ranzato/publications/ranzato_cvpr2010.pdf] M. Ranzato, G. Hinton, Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. CVPR 2010  
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** (someone and tell us if you need to read the 3-way RBM paper before the mcRBM one)
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** (someone and tell us if you need to read the 3-way RBM paper before the mcRBM one [I didn't find it necessary, in fact the CVPR paper seemed easier to understand.])
* [http://www.cs.toronto.edu/~hinton/absps/mcphone.pdf] Dahl, G., Ranzato, M., Mohamed, A. and Hinton, G. E. Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS 2010.
* [http://www.cs.toronto.edu/~hinton/absps/mcphone.pdf] Dahl, G., Ranzato, M., Mohamed, A. and Hinton, G. E. Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS 2010.
* [http://www.nature.com/nature/journal/v457/n7225/pdf/nature07481.pdf] Y. Karklin and M. S. Lewicki, Emergence of complex cell properties by learning to generalize in natural scenes, Nature, 2008.
* [http://www.nature.com/nature/journal/v457/n7225/pdf/nature07481.pdf] Y. Karklin and M. S. Lewicki, Emergence of complex cell properties by learning to generalize in natural scenes, Nature, 2008.
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** (someone tell us if this should be here.  Interesting algorithm + nice visualizations, though maybe slightly hard to understand.)  
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** (someone tell us if this should be here.  Interesting algorithm + nice visualizations, though maybe slightly hard to understand. [seems a good reminder there are other existing models])  

Latest revision as of 07:00, 18 February 2012

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