19 Publikationen
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2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296Velioglu, R., et al., 2022. Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. no.13756 Cham: Springer International Publishing, pp. 313-325.PUB | DOI
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385Risse, N., Göpfert, C., & Göpfert, J.P., 2021. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In I. Farkaš, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. no.12891 Cham: Springer International Publishing, pp. 29-41.PUB | DOI
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2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245Stallmann, D., et al., Accepted. Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation. Bioinformatics .PUB | DOI | WoS | PubMed | Europe PMC
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094Limberg, C., et al., 2020. Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In I. Farkaš, P. Masulli, & S. Wermter, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. no.12397 Cham: Springer International Publishing, pp. 204-213.PUB | DOI
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2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085Göpfert, J.P., Wersing, H., & Hammer, B., 2019. Recovering Localized Adversarial Attacks. In I. V. Tetko, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 302-311.PUB | DOI
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2018 | Datenpublikation | PUB-ID: 2930611Hülsmann, F., et al., 2018. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data), Bielefeld University.PUB | Dateien verfügbar | DOI
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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862Hülsmann, F., et al., 2018. Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes. Computers & Graphics, 76, p 47-59.PUB | DOI | Download (ext.) | WoS
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316Göpfert, J.P., Hammer, B., & Wersing, H., 2018. Mitigating Concept Drift via Rejection. In V. Kurkova, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. no.11139 Cham: Springer.PUB | PDF | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752Göpfert, J.P., et al., 2017. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.PUB | PDF | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274Göpfert, C., Göpfert, J.P., & Hammer, B., 2017. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.PUB | PDF
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2015 | Report | PUB-ID: 2712107Stöckel, A., et al., 2015. SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit, bioRxive.org.PUB | PDF | DOI | Download (ext.)
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760Paaßen, B., et al., 2014. Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. In D. Maynard, M. Erp van, & B. Davis, eds. Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING). Dublin, Ireland, pp. 25-32.PUB | PDF | Download (ext.)