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We present a representation learning method that learns features at multiple different levels of scale.
Numerical continuation methods. An introduction
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Earlier work this paper cites.
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Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
A connection between score matching and denoising autoencoders
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Unsupervised and transfer learning challenge: a deep learning approach
Grégoire Mesnil, Yann Dauphin, Xavier Glorot, Salah Rifai, Yoshua Bengio, Ian J. Goodfellow, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, Pascal Vincent, Aaron C. Courville, and James Bergstra · 2012
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Generalized denoising auto-encoders as generative models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
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Fastfood-computing Hilbert space expansions in loglinear time
Quoc Le, Tamás Sarlós, and Alexander Smola · 2013
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Adaptive noise schedule for denoising autoencoder
B. Chandra and Rajesh Kumar Sharma · 2014
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Improving neural networks by preventing co-adaptation of feature detectors
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Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Y. Ng, and Honglak Lee · 2011
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Efficient coding of natural images with a population of noisy linear-nonlinear neurons
Yan Karklin and Eero P. Simoncelli · 2011
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Deep sparse rectifier networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio
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Domain adaptation for large-scale sentiment classification: A deep learning approach
Xavier Glorot, Antoine Bordes, and Yoshua Bengio
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Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Zero-bias autoencoders and the benefits of co-adapting features
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Annealed dropout training of deep networks
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