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Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning.
Efficient backprop
Y. Lecun, L. Bottou, G. B. Orr, and K.-R. Müller · 1998
Earlier work this paper cites.
Efficient backprop
Y. Lecun, L. Bottou, G. B. Orr, and K.-R. Müller · 1998
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Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Learning deep architectures for ai
Y. Bengio · 2009
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Learning deep architectures for ai
Y. Bengio · 2009
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Contractive auto-encoders: Explicit invariance during feature extraction
S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio · 2011
Earlier work this paper cites.
Contractive auto-encoders: Explicit invariance during feature extraction
S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio · 2011
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
D.-H. Lee · 2013
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
D.-H. Lee · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Winner-take-all autoencoders
A. Makhzani and B. J. Frey · 2015
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Distributional smoothing by virtual adversarial examples
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
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A probabilistic theory of deep learning
A. B. Patel, T. Nguyen, and R. G. Baraniuk · 2015
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Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2015
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Variational inference: A review for statisticians
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe · 2016
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Early visual concept learning with unsupervised deep learning
I. Higgins, L. Matthey, X. Glorot, A. Pal, B. Uria, C. Blundell, S. Mohamed, and A. Lerchner · 2016
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Auxiliary deep generative models
L. Maaløe, C. K. Sønderby, S. K. Sønderby, and O. Winther · 2016
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A probabilistic framework for deep learning
A. B. Patel, T. Nguyen, and R. G. Baraniuk · 2016
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Winner-take-all autoencoders
A. Makhzani and B. J. Frey · 2015
Cited alongside, same era.
Distributional smoothing by virtual adversarial examples
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
Cited alongside, same era.
A probabilistic theory of deep learning
A. B. Patel, T. Nguyen, and R. G. Baraniuk · 2015
Cited alongside, same era.
Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
Cited alongside, same era.
Closest in time.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Stacked what-where autoencoders
J. Zhao, M. Mathieu, R. Goroshin, and Y. LeCun · 2016
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Variational inference: A review for statisticians
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe · 2016
Closest in time.
Early visual concept learning with unsupervised deep learning
I. Higgins, L. Matthey, X. Glorot, A. Pal, B. Uria, C. Blundell, S. Mohamed, and A. Lerchner · 2016
Closest in time.
Auxiliary deep generative models
L. Maaløe, C. K. Sønderby, S. K. Sønderby, and O. Winther · 2016
Closest in time.
A probabilistic framework for deep learning
A. B. Patel, T. Nguyen, and R. G. Baraniuk · 2016
Closest in time.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Closest in time.
Stacked what-where autoencoders
J. Zhao, M. Mathieu, R. Goroshin, and Y. LeCun · 2016
Closest in time.