Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification with Runge-Kutta networks and feature space augmentation   In this paper we combine an approach based on Runge-Kutta Nets considered in
[Benning et al., J. Comput. Dynamics, 9, 2019] and a technique on augmenting
the input space in [Dupont et al., NeurIPS, 2019] to obtain network
architectures which show a better numerical performance for deep neural
networks in point and image classification problems. The approach is
illustrated with several examples implemented in PyTorch.

6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
16
The categories of this paper are: stat