UFLDL Recommended Readings
From Ufldl
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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]. | ||
- | * [http:// | + | * [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 | + | ** (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. | ||
- | ** (Someone read this and let us know if this is worth keeping,.) | + | ** (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. | ||
- | ** (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?) | + | ** (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. | ||
+ | * [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 | ||
+ | * [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 | ||
- | ** (someone and tell us if you need to read the 3-way RBM paper before the mcRBM one) | + | ** (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. | ||
- | ** (someone tell us if this should be here. Interesting algorithm + nice visualizations, though maybe slightly hard to understand.) | + | ** (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]) |