Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Maximally Invariant Data Perturbation as Explanation   While several feature scoring methods are proposed to explain the output of
complex machine learning models, most of them lack formal mathematical
definitions. In this study, we propose a novel definition of the feature score
using the maximally invariant data perturbation, which is inspired from the
idea of adversarial example. In adversarial example, one seeks the smallest
data perturbation that changes the model's output. In our proposed approach, we
consider the opposite: we seek the maximally invariant data perturbation that
does not change the model's output. In this way, we can identify important
input features as the ones with small allowable data perturbations. To find the
maximally invariant data perturbation, we formulate the problem as linear
programming. The experiment on the image classification with VGG16 shows that
the proposed method could identify relevant parts of the images effectively.

10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom