UFLDL Recommended Readings
From Ufldl
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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, not many conclusions can be made from results]) |
* [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 to start with, and have to deal with Hinton's 06 Science paper or 3-way RBM's]) |