Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Information Acquisition   We propose a general framework for sequential and dynamic acquisition of
useful information in order to solve a particular task. While our goal could in
principle be tackled by general reinforcement learning, our particular setting
is constrained enough to allow more efficient algorithms. In this paper, we
work under the Learning to Search framework and show how to formulate the goal
of finding a dynamic information acquisition policy in that framework. We apply
our formulation on two tasks, sentiment analysis and image recognition, and
show that the learned policies exhibit good statistical performance. As an
emergent byproduct, the learned policies show a tendency to focus on the most
prominent parts of each instance and give harder instances more attention
without explicitly being trained to do so.

12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con