Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Targeted Deep Learning: Framework, Methods, and Applications   Deep learning systems are typically designed to perform for a wide range of
test inputs. For example, deep learning systems in autonomous cars are supposed
to deal with traffic situations for which they were not specifically trained.
In general, the ability to cope with a broad spectrum of unseen test inputs is
called generalization. Generalization is definitely important in applications
where the possible test inputs are known but plentiful or simply unknown, but
there are also cases where the possible inputs are few and unlabeled but known
beforehand. For example, medicine is currently interested in targeting
treatments to individual patients; the number of patients at any given time is
usually small (typically one), their diagnoses/responses/... are still unknown,
but their general characteristics (such as genome information, protein levels
in the blood, and so forth) are known before the treatment. We propose to call
deep learning in such applications targeted deep learning. In this paper, we
introduce a framework for targeted deep learning, and we devise and test an
approach for adapting standard pipelines to the requirements of targeted deep
learning. The approach is very general yet easy to use: it can be implemented
as a simple data-preprocessing step. We demonstrate on a variety of real-world
data that our approach can indeed render standard deep learning faster and more
accurate when the test inputs are known beforehand.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph