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Poster
Learning Algorithms for Active Learning
Philip Bachman · Alessandro Sordoni · Adam Trischler
We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a prediction function. Our model uses the item selection heuristic to construct a labeled support set for training the prediction function. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.
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Author Information
Philip Bachman (Maluuba)
Alessandro Sordoni (Microsoft Maluuba)
Adam Trischler (Maluuba)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Talk: Learning Algorithms for Active Learning »
Mon. Aug 7th 08:09 -- 08:27 AM Room Darling Harbour Theatre
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