Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Learning in CNNs via Expected Improvement Maximization   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.

12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat