Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dissecting Neural ODEs   Continuous deep learning architectures have recently re-emerged as Neural
Ordinary Differential Equations (Neural ODEs). This infinite-depth approach
theoretically bridges the gap between deep learning and dynamical systems,
offering a novel perspective. However, deciphering the inner working of these
models is still an open challenge, as most applications apply them as generic
black-box modules. In this work we "open the box", further developing the
continuous-depth formulation with the aim of clarifying the influence of
several design choices on the underlying dynamics.

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The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
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The categories of this paper are: eess
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an