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In this paper we present a technique to train neural network models on small amounts of data.
Direct importance estimation with model selection and its application to covariate shift adaptation
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Grégoire Mesnil, Yann Dauphin, Xavier Glorot, Salah Rifai, Yoshua Bengio, Ian J Goodfellow, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, et al · 2012
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Simultaneous deep transfer across domains and tasks
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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