19 Publikationen

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  • [19]
    2022 | Bielefelder E-Dissertation | PUB-ID: 2967691 OA
    Göpfert, J. P. (2022). Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2967691
    PUB | PDF | DOI
     
  • [18]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296
    Velioglu, 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_31
    PUB | DOI
     
  • [17]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961873
    Göpfert, J. P., Wersing, H., & Hammer, B. (2022). Interpretable locally adaptive nearest neighbors. Neurocomputing, 470, 344-351. https://doi.org/10.1016/j.neucom.2021.05.105
    PUB | DOI | WoS
     
  • [16]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
    Risse, 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_3
    PUB | DOI
     
  • [15]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    Göpfert, 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.2021.3121666
    PUB | DOI | WoS
     
  • [14]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2961876
    Hindemith, 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.hin
    PUB | DOI | WoS
     
  • [13]
    2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245
    Stallmann, 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/btab386
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [12]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949926 OA
    Mohanty, 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.2020.534696
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [11]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094
    Limberg, 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_17
    PUB | DOI
     
  • [10]
    2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085
    Göpfert, 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_24
    PUB | DOI
     
  • [9]
    2019 | Preprint | Veröffentlicht | PUB-ID: 2934181
    Göpfert, J. P., Wersing, H., & Hammer, B. (2019). Adversarial attacks hidden in plain sight. doi:10.4119/unibi/2934181
    PUB | DOI | arXiv
     
  • [8]
    2018 | Datenpublikation | PUB-ID: 2930611 OA
    Hülsmann, 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/2930611
    PUB | Dateien verfügbar | DOI
     
  • [7]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862
    Hülsmann, 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.2018.08.003
    PUB | DOI | Download (ext.) | WoS
     
  • [6]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316 OA
    Göpfert, 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_45
    PUB | PDF | DOI
     
  • [5]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    Göpfert, C., Pfannschmidt, L., Göpfert, J. P., & Hammer, B. (2018). Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, 69-79. doi:10.1016/j.neucom.2017.11.074
    PUB | PDF | DOI | WoS
     
  • [4]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
    Göpfert, 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.2017.8285305
    PUB | PDF | DOI
     
  • [3]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
    Göpfert, 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 Environments
    PUB | PDF
     
  • [2]
    2015 | Report | PUB-ID: 2712107 OA
    Stöckel, 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:10.1101/013458
    PUB | PDF | DOI | Download (ext.)
     
  • [1]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760 OA
    Paaßen, 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.
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