Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series   Modeling real-world multidimensional time series can be particularly
challenging when these are sporadically observed (i.e., sampling is irregular
both in time and across dimensions)-such as in the case of clinical patient
data. To address these challenges, we propose (1) a continuous-time version of
the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential
Equations (Chen et al., 2018), and (2) a Bayesian update network that processes
the sporadic observations. We bring these two ideas together in our
GRU-ODE-Bayes method. We then demonstrate that the proposed method encodes a
continuity prior for the latent process and that it can exactly represent the
Fokker-Planck dynamics of complex processes driven by a multidimensional
stochastic differential equation. Additionally, empirical evaluation shows that
our method outperforms the state of the art on both synthetic data and
real-world data with applications in healthcare and climate forecast. What is
more, the continuity prior is shown to be well suited for low number of samples
settings.

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The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess