19 Publikationen
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2022 | Bielefelder E-Dissertation | PUB-ID: 2967691Göpfert, J. P. (2022). Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2967691PUB | PDF | DOI
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2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296Velioglu, R., Göpfert, J. P., Artelt, A., & Hammer, B. (2022). Explainable Artificial Intelligence for Improved Modeling of Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 313-325). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_31PUB | DOI
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2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961873Göpfert, J. P., Wersing, H., & Hammer, B. (2022). Interpretable locally adaptive nearest neighbors. Neurocomputing, 470, 344-351. https://doi.org/10.1016/j.neucom.2021.05.105PUB | DOI | WoS
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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, P. Masulli, S. Otte, & S. Wermter (Eds.), Lecture Notes in Computer Science: Vol. 12891. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I (pp. 29-41). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-86362-3_3PUB | DOI
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418Göpfert, J. P., Kuhl, U., Hindemith, L., Wersing, H., & Hammer, B. (2021). Intuitiveness in Active Teaching. IEEE Transactions on Human-Machine Systems, 1-10. https://doi.org/10.1109/THMS.2021.3121666PUB | DOI | WoS
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961876Hindemith, L., Göpfert, J. P., Wiebel-Herboth, C. B., Wrede, B., & Vollmer, A. - L. (2021). Why robots should be technical correcting mental models through technical architecture concepts. Interaction Studies , 22(2), 244-279. https://doi.org/10.1075/is.20023.hinPUB | DOI | WoS
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2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245Stallmann, D., Göpfert, J. P., Schmitz, J., Grünberger, A., & Hammer, B. (Accepted). Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation. Bioinformatics . https://doi.org/10.1093/bioinformatics/btab386PUB | DOI | WoS | PubMed | Europe PMC
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2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949926Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., Rohrbach, J., et al. (2020). Deep Learning for Understanding Satellite Imagery: An Experimental Survey. Frontiers in Artificial Intelligence, 3, 534696. https://doi.org/10.3389/frai.2020.534696PUB | PDF | DOI | WoS | PubMed | Europe PMC
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094Limberg, C., Göpfert, J. P., Wersing, H., & Ritter, H. (2020). Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Lecture Notes in Computer Science: Vol. 12397. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II (pp. 204-213). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-61616-8_17PUB | 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, V. Kůrková, P. Karpov, & F. Theis (Eds.), Lecture Notes in Computer Science. 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 (pp. 302-311). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-30487-4_24PUB | DOI
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2018 | Datenpublikation | PUB-ID: 2930611Hülsmann, F., Göpfert, J. P., Hammer, B., Kopp, S., & Botsch, M. (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. doi:10.4119/unibi/2930611PUB | Dateien verfügbar | DOI
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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862Hülsmann, F., Göpfert, J. P., Hammer, B., Kopp, S., & Botsch, M. (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, 47-59. doi:10.1016/j.cag.2018.08.003PUB | 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, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Lecture Notes in Computer Science: Vol. 11139. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I Cham: Springer. doi:10.1007/978-3-030-01418-6_45PUB | PDF | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752Göpfert, J. P., Göpfert, C., Botsch, M., & Hammer, B. (2017). Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Piscataway, NJ: IEEE. doi:10.1109/SSCI.2017.8285305PUB | 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. Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical EnvironmentsPUB | PDF
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2015 | Report | PUB-ID: 2712107Stöckel, A., Paaßen, B., Dickfelder, R., Göpfert, J. P., Brazda, N., Müller, H. W., Cimiano, P., et al. (2015). SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. bioRxive.org. doi:10.1101/013458PUB | PDF | DOI | Download (ext.)
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760Paaßen, B., Stöckel, A., Dickfelder, R., Göpfert, J. P., Brazda, N., Kirchhoffer, T., Müller, H. W., 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) (pp. 25-32). Dublin, Ireland.PUB | PDF | Download (ext.)