Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Ordinary Differential Equations for Intervention Modeling   By interpreting the forward dynamics of the latent representation of neural
networks as an ordinary differential equation, Neural Ordinary Differential
Equation (Neural ODE) emerged as an effective framework for modeling a system
dynamics in the continuous time domain. However, real-world systems often
involves external interventions that cause changes in the system dynamics such
as a moving ball coming in contact with another ball, or such as a patient
being administered with particular drug. Neural ODE and a number of its recent
variants, however, are not suitable for modeling such interventions as they do
not properly model the observations and the interventions separately. In this
paper, we propose a novel neural ODE-based approach (IMODE) that properly model
the effect of external interventions by employing two ODE functions to
separately handle the observations and the interventions. Using both synthetic
and real-world time-series datasets involving interventions, our experimental
results consistently demonstrate the superiority of IMODE compared to existing
approaches.

5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom