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We combine supervised learning with unsupervised learning in deep neural networks.
Dropout: A simple way to prevent neural networks from overfitting
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Combining labeled and unlabeled data with co-training
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The value of unlabeled data for classification problems
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Partially labeled classification with Markov random walks
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Denoising source separation
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Semi-supervised learning
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Reducing the dimensionality of data with neural networks
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Semi-supervised learning of compact document representations with deep networks
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Theano: a CPU and GPU math expression compiler
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
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The manifold tangent classifier
Rifai, S., Dauphin, Y. N., Vincent, P., Bengio, Y., and Muller, X. (2011) · 2011
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Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012) · 2012
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Large-scale feature learning with spike-and-slab sparse coding
Goodfellow, I., Bengio, Y., and Courville, A. C. (2012) · 2012
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Deep learning via semi-supervised embedding
Weston, J., Ratle, F., Mobahi, H., and Collobert, R. (2012) · 2012
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Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013) · 2013
Semi-supervised learning using an unsupervised atlas
Pitelis, N., Russell, C., and Agapito, L. (2014) · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A. (2014) · 2014
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Stacked what-where auto-encoders
Zhao, J., Mathieu, M., Goroshin, R., and Lecun, Y. (2015) · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Multi-prediction deep Boltzmann machines
Goodfellow, I., Mirza, M., Courville, A., and Bengio, Y. (2013a) · 2013
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Maxout networks
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013b) · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. (2013) · 2013
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How auto-encoders could provide credit assignment in deep networks via target propagation
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Discriminative unsupervised feature learning with convolutional neural networks
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Deep autoregressive networks
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D. (2014) · 2014
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Adam: A method for stochastic optimization
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Deconstructing the ladder network architecture
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Techniques for learning binary stochastic feedforward neural networks
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From neural PCA to deep unsupervised learning
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Blocks and fuel: Frameworks for deep learning
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