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In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised portion of the training.
Flexible manifold embedding: A framework for semi-supervised and unsupervised dimension reduction
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The mnist database of handwritten digits
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Feature selection, l 1 vs. l 2 regularization, and rotational invariance
Ng, A. Y. (2004) · 2004
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Face recognition using laplacianfaces
He, X., Yan, S., Hu, Y., Niyogi, P., and Zhang, H. (2005) · 2005
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Belkin, M., Niyogi, P., and Sindhwani, V. (2006) · 2006
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Hadsell, R., Chopra, S., and LeCun, Y. (2006) · 2006
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A fast learning algorithm for deep belief nets
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G. (2008) · 2008
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Ranzato, M. and Szummer, M. (2008) · 2008
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Bottou, L. (2010) · 2010
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Large graph construction for scalable semi-supervised learning
Liu, W., He, J., and Chang, S. (2010) · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011) · 2011
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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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Distributional smoothing by virtual adversarial examples
Miyato, T., Maeda, S., Koyama, M., Nakae, K., and Ishii, S. (2015) · 2015
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Semi-supervised learning with ladder networks
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., and Raiko, T. (2015) · 2015
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., and Courville, A. C. (2016) · 2016
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Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T. (2016) · 2016
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Auto-encoding variational bayes
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y. (2014) · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
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Valpola, H. (2014) · 2014
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Learning on big graph: Label inference and regularization with anchor hierarchy
Wang, M., Fu, W., Hao, S., Liu, H., and Wu, X. (2017a)
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Auxiliary deep generative models
Maaløe, L., Sønderby, C. K., Sønderby, S. K., and Winther, O. (2016) · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
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Theano: A Python framework for fast computation of mathematical expressions
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Scalable semi-supervised learning by efficient anchor graph regularization
Wang, M., Fu, W., Hao, S., Tao, D., and Wu, X. (2016) · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R. (2016) · 2016
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S., Koyama, M., and Ishii, S. (2017) · 2017
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