Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainable Neural Networks based on Additive Index Models   Machine Learning algorithms are increasingly being used in recent years due
to their flexibility in model fitting and increased predictive performance.
However, the complexity of the models makes them hard for the data analyst to
interpret the results and explain them without additional tools. This has led
to much research in developing various approaches to understand the model
behavior. In this paper, we present the Explainable Neural Network (xNN), a
structured neural network designed especially to learn interpretable features.
Unlike fully connected neural networks, the features engineered by the xNN can
be extracted from the network in a relatively straightforward manner and the
results displayed. With appropriate regularization, the xNN provides a
parsimonious explanation of the relationship between the features and the
output. We illustrate this interpretable feature--engineering property on
simulated examples.

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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat