Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How to Explain Neural Networks: an Approximation Perspective   The lack of interpretability has hindered the large-scale adoption of AI
technologies. However, the fundamental idea of interpretability, as well as how
to put it into practice, remains unclear. We provide notions of
interpretability based on approximation theory in this study. We first
implement this approximation interpretation on a specific model (fully
connected neural network) and then propose to use MLP as a universal
interpreter to explain arbitrary black-box models. Extensive experiments
demonstrate the effectiveness of our approach.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph