Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal Autoencoder with U-Net Style Skip-Connections for Frame
  Prediction   Finding sustainable and novel solutions to predict city-wide mobility
behaviour is an ever-growing problem given increased urban complexity and
growing populations. This paper seeks to address this by describing a traffic
frame prediction approach that uses Convolutional LSTMs to create a Temporal
Autoencoder with U-Net style skip-connections that marry together recurrent and
traditional computer vision techniques to capture spatio-temporal dependencies
at different scales without losing topological details of a given city.
Utilisation of Cyclical Learning Rates is also presented, improving training
efficiency by achieving lower loss scores in fewer epochs than standard
approaches.

18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph