Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Demystifying Deep Neural Networks Through Interpretation: A Survey   Modern deep learning algorithms tend to optimize an objective metric, such as
minimize a cross entropy loss on a training dataset, to be able to learn. The
problem is that the single metric is an incomplete description of the real
world tasks. The single metric cannot explain why the algorithm learn. When an
erroneous happens, the lack of interpretability causes a hardness of
understanding and fixing the error. Recently, there are works done to tackle
the problem of interpretability to provide insights into neural networks
behavior and thought process. The works are important to identify potential
bias and to ensure algorithm fairness as well as expected performance.

34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol