Learning Algorithms for Active Learning
Philip Bachman ⋅ Alessandro Sordoni ⋅ Adam Trischler
2017 Poster
[
PDF]
[
Summary/Notes]
Summary/Notes]
Abstract
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.
Chat is not available.
Successful Page Load