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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… Expand We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to… Expand The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised… Expand If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word… Expand Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans… Expand In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in… Expand This chapter contains sections titled: Supervised, Unsupervised, and Semi-Supervised Learning, When Can Semi-Supervised Learning… Expand In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled… Expand We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this… Expand An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data… Expand