Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entropy-based Logic Explanations of Neural Networks   Explainable artificial intelligence has rapidly emerged since lawmakers have
started requiring interpretable models for safety-critical domains.
Concept-based neural networks have arisen as explainable-by-design methods as
they leverage human-understandable symbols (i.e. concepts) to predict class
memberships. However, most of these approaches focus on the identification of
the most relevant concepts but do not provide concise, formal explanations of
how such concepts are leveraged by the classifier to make predictions. In this
paper, we propose a novel end-to-end differentiable approach enabling the
extraction of logic explanations from neural networks using the formalism of
First-Order Logic. The method relies on an entropy-based criterion which
automatically identifies the most relevant concepts. We consider four different
case studies to demonstrate that: (i) this entropy-based criterion enables the
distillation of concise logic explanations in safety-critical domains from
clinical data to computer vision; (ii) the proposed approach outperforms
state-of-the-art white-box models in terms of classification accuracy and
matches black box performances.

31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
18
The categories of this paper are: econ