Semi-supervised deep kernel learning
نویسندگان
چکیده
Deep learning techniques have led to massive improvements in recent years, but large amounts of labeled data are typically required to learn these complex models. We present a semi-supervised approach for training deep models that combines the feature learning capabilities of neural networks with the probabilistic modeling of Gaussian processes and demonstrate that unlabeled data can significantly improve performance on real-world datasets.
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