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Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. (arXiv:2203.17070v2 [cs.LG] UPDATED)
April 4, 2022, 1:12 a.m. | Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Alekse
cs.LG updates on arXiv.org arxiv.org
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that
neural networks can successfully predict future traffic conditions 1 hour into
the future on simply aggregated GPS probe data in time and space bins. We thus
reinterpreted the challenge of forecasting traffic conditions as a movie
completion task. U-Nets proved to be the winning architecture, demonstrating an
ability to extract relevant features in this complex real-world geo-spatial
process. Building on the previous competitions, Traffic4cast 2021 now focuses
on the …
arxiv learning neurips neurips 2021 processes transfer learning
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