Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A general approach to compute the relevance of middle-level input
  features   This work proposes a novel general framework, in the context of eXplainable
Artificial Intelligence (XAI), to construct explanations for the behaviour of
Machine Learning (ML) models in terms of middle-level features. One can isolate
two different ways to provide explanations in the context of XAI: low and
middle-level explanations. Middle-level explanations have been introduced for
alleviating some deficiencies of low-level explanations such as, in the context
of image classification, the fact that human users are left with a significant
interpretive burden: starting from low-level explanations, one has to identify
properties of the overall input that are perceptually salient for the human
visual system. However, a general approach to correctly evaluate the elements
of middle-level explanations with respect ML model responses has never been
proposed in the literature.

13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph