Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Matrix Completion With Variational Graph Autoencoders: Application in
  Hyperlocal Air Quality Inference   Inferring air quality from a limited number of observations is an essential
task for monitoring and controlling air pollution. Existing inference methods
typically use low spatial resolution data collected by fixed monitoring
stations and infer the concentration of air pollutants using additional types
of data, e.g., meteorological and traffic information. In this work, we focus
on street-level air quality inference by utilizing data collected by mobile
stations. We formulate air quality inference in this setting as a graph-based
matrix completion problem and propose a novel variational model based on graph
convolutional autoencoders. Our model captures effectively the spatio-temporal
correlation of the measurements and does not depend on the availability of
additional information apart from the street-network topology. Experiments on a
real air quality dataset, collected with mobile stations, shows that the
proposed model outperforms state-of-the-art approaches.

8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga