Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Making Neural Networks FAIR   Research on neural networks has gained significant momentum over the past few
years. Because training is a resource-intensive process and training data
cannot always be made available to everyone, there has been a trend to reuse
pre-trained neural networks. As such, neural networks themselves have become
research data. In this paper, we first present the neural network ontology
FAIRnets Ontology, an ontology to make existing neural network models findable,
accessible, interoperable, and reusable according to the FAIR principles. Our
ontology allows us to model neural networks on a meta-level in a structured
way, including the representation of all network layers and their
characteristics. Secondly, we have modeled over 18,400 neural networks from
GitHub based on this ontology, which we provide to the public as a knowledge
graph called FAIRnets, ready to be used for recommending suitable neural
networks to data scientists.

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The categories of this paper are: quant-ph
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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: patt-sol
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The categories of this paper are: astro-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
3
The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an