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(2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In <i>2021 3rd International Conference on Management Science and Industrial Engineering</i> (pp. 1–7). New York, NY, USA: ACM. <a href=\"https://doi.org/10.1145/3460824.3460825\">https://doi.org/10.1145/3460824.3460825</a>","bibtex":"@inproceedings{Steinmann_Migenda_Voigt_Kohlhase_Schenck_2021, place={New York, NY, USA}, title={Variational Autoencoder based Novelty Detection for Real-World Time Series}, DOI={<a href=\"https://doi.org/10.1145/3460824.3460825\">10.1145/3460824.3460825</a>}, booktitle={2021 3rd International Conference on Management Science and Industrial Engineering}, publisher={ACM}, author={Steinmann, Luca and Migenda, Nico and Voigt, Tim and Kohlhase, Martin and Schenck, Wolfram}, year={2021}, pages={1–7} }","alphadin":"<span style=\"font-variant:small-caps;\">Steinmann, Luca</span> ; <span style=\"font-variant:small-caps;\">Migenda, Nico</span> ; <span style=\"font-variant:small-caps;\">Voigt, Tim</span> ; <span style=\"font-variant:small-caps;\">Kohlhase, Martin</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: Variational Autoencoder based Novelty Detection for Real-World Time Series. 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