Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
Feb 17, 2017 (modified: Mar 17, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:We present a model that learns active learning algorithms via metalearning. For each metatask, our model jointly learns: a data representation, an item selection heuristic, and a one-shot classifier. Our model uses the item selection heuristic to construct a labeled support set for the one-shot classifier. Using metatasks based on the Omniglot and MovieLens datasets, we show that our model performs well in synthetic and practical settings.
TL;DR:We present a model and experiments for meta active learning.
Keywords:Deep learning, Supervised Learning
Enter your feedback below and we'll get back to you as soon as possible.