  Active Learning techniques are used to tackle learning problems where
obtaining training labels is costly. In this work we use Meta-Active Learning
to learn to select a subset of samples from a pool of unsupervised input for
further annotation. This scenario is called Static Pool-based Meta- Active
Learning. We propose to extend existing approaches by performing the selection
in a manner that, unlike previous works, can handle the selection of each
sample based on the whole selected subset.
