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Learning representations of data, and in particular learning features for a subsequent prediction task, has been a fruitful area of research delivering impressive empirical results in recent years.
Extensions of Lipschitz mappings into a Hilbert space
William B. Johnson and Joram Lindenstrauss · 1984
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Reducing the dimensionality of data with neural networks
Geoffrey E. Hinton and Ruslan R. Salakhutdinov · 2006
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Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
Philippe Rigollet · 2007
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Unlabeled data: Now it helps, now it doesn’t
Aarti Singh, Robert Nowak, and Xiaojin Zhu · 2009
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A discriminative model for semi-supervised learning
Maria-Florina Balcan and Avrim Blum · 2010
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Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Semi-supervised learning with density based distances
Avleen S. Bijral, Nathan Ratliff, and Nathan Srebro · 2012
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Elements of Information Theory
Thomas M. Cover and Joy A. Thomas · 2012
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Representation Learning: A Review and New Perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean · 2013
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The Nature of Statistical Learning Theory
Vladimir Vapnik · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Towards Principled Unsupervised Learning
Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, Danilo Rezende, Tim Lillicrap, and Oriol Vinyals · 2015
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Brendan van Rooyen and Robert C. Williamson · 2015
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