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6 Publications
2023 | Conference Paper | LibreCat-ID: 52816
I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing,” in Lectures, Nuremberg, 2023, pp. 253–524, doi: 10.5162/smsi2023/d7.4.
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| DOI
2021 | Journal Article | LibreCat-ID: 20212 | 
J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach,” Decision Support Systems, vol. 140, no. January, Art. no. 113432, 2021.
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2021 | Conference Paper | LibreCat-ID: 24547
O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data,” presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.
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2020 | Conference Paper | LibreCat-ID: 19609 |
S. B. Schneider et al., “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning,” in IEEE International Conference on Network and Service Management (CNSM), 2020.
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2020 | Conference Paper | LibreCat-ID: 15580
J. Kersting and M. Geierhos, “Aspect Phrase Extraction in Sentiment Analysis with Deep Learning,” in Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020), Valetta, Malta, 2020, pp. 391--400.
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2020 | Conference Paper | LibreCat-ID: 48897
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in Parallel Problem Solving from {Nature} (PPSN XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.
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