Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Concept-Oriented Deep Learning: Generative Concept Representations   Generative concept representations have three major advantages over
discriminative ones: they can represent uncertainty, they support integration
of learning and reasoning, and they are good for unsupervised and
semi-supervised learning. We discuss probabilistic and generative deep
learning, which generative concept representations are based on, and the use of
variational autoencoders and generative adversarial networks for learning
generative concept representations, particularly for concepts whose data are
sequences, structured data or graphs.

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The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th