UFLDL Recommended Readings

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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.

-

** (Someone read this and let us know if this is worth keeping,. [~~Could serve as a easy reinforcement to other papers, but most~~ model related material already covered by other papers, results ~~do not seem to have strong impact~~])

+

** (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://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.

** 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? [~~Agree~~ to ~~move~~ to ~~later~~])

+

** (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])

UFLDL Recommended Readings

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@@ Line 20: / Line 20: @@
 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.
-** (Someone read this and let us know if this is worth keeping,. [Could serve as a easy reinforcement to other papers, but most model related material already covered by other papers, results do not seem to have strong impact])
+** (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://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.
@@ Line 28: / Line 28: @@
 * [http://deeplearning.net/tutorial/rbm.html] Tutorial on RBMs.
 ** 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? [Agree to move to later])
+** (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])