UFLDL Recommended Readings
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If you're learning about UFLDL (Unsupervised Feature Learning and Deep Learning), here is a list of papers to consider reading. The basics: * [[http://cs294a.stanford.edu CS294A]] neural network/sparse autoencoder tutorial. (Most of this is now in the [[UFLDL Tutorial]], but the exercise is still on the CS294A website.) * Natural Image Statistics book, Hyvarinen et al. * Olshausen and Field Sparse Coding paper (1996) * Learning Deep Architectures for AI. (Broad landscape description of the field, but technical details there are hard to follow so ignore that.) * Rajat Raina's Self-Taught Learning paper from ICML 2009. (2008?) Papers on autoencoders: * Greedy layerwise training of autoencoders * 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 * Larochelle, Erhan, Courville, Bergstra, BBengio, 2007. (Someone read this and let us know if this is worth keeping,.) * Salakhutdinov and Hinton Science paper * Bengio's denoising autoencoders paper from ICML * Papers on SFA: * Wiskott, L and Sejnowski, T (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation 14(4): 715-770. Papers on RBMs/DBNs: * Tutorial on RBMs. But ignore the Theonu (or whatever the language is called) code examples. http://deeplearning.net/tutorial/rbm.html * If you're implemeting RBMs, then read Hinton's "practical guide to RBMs." But otherwise not great as a tutorial. Advanced stuff: * LeCun's "what is the best multi-stage architecture?" paper from CVPR. * Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR 2009 NLP stuff: * Neural Language Model (Bengio, ~2003?) Don't remember how readable this is. If it's not,someone please take this out. * Collobert and Weston, 2008 * Mnih and Hinton "3 new models for something or other", in ICML Also, for other lists of papers: * Honglak Lee's course
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