  Deep learning models such as Convolutional Neural Networks (CNNs) have
demonstrated high levels of effectiveness in a variety of domains, including
computer vision and more recently, computational biology. However, training
effective models often requires assembling and/or labeling large datasets,
which may be prohibitively time-consuming or costly. Pool-based active learning
techniques have the potential to mitigate these issues, leveraging models
trained on limited data to selectively query unlabeled data points from a pool
in an attempt to expedite the learning process. Here we present "Dropout-based
Expected IMprOvementS" (DEIMOS), a flexible and computationally-efficient
approach to active learning that queries points that are expected to maximize
the model's improvement across a representative sample of points. The proposed
framework enables us to maintain a prediction covariance matrix capturing model
uncertainty, and to dynamically update this matrix in order to generate diverse
batches of points in the batch-mode setting. Our active learning results
demonstrate that DEIMOS outperforms several existing baselines across multiple
regression and classification tasks taken from computer vision and genomics.
