PUB - Publikationen an der Universität Bielefeld

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• [19]
  

  2022 | Bielefelder E-Dissertation | PUB-ID: 2967691

  Göpfert, J.P. (2022). Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition. Bielefeld: Universität Bielefeld. doi: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 (Lecture Notes in Computer Science). In H. Yin, D. Camacho & P. Tino (Hrsg.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (S. 313-325). Gehalten auf der 23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2022), Cham: Springer International Publishing. doi: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. Elsevier. doi: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 (Lecture Notes in Computer Science). In I. Farkaš, P. Masulli, S. Otte & S. Wermter (Hrsg.), Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I (S. 29-41). Gehalten auf der 30th International Conference on Artificial Neural Networks (ICANN 2021), Cham: Springer International Publishing. doi: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. Institute of Electrical and Electronics Engineers (IEEE). doi: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. John Benjamins. doi: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 . Oxford University Press. doi:10.1093/bioinformatics/btab386.

  PUB | DOI | WoS | PubMed | Europe PMC
   
• [12]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949926

  Mohanty, S.P., Czakon, J., Kaczmarek, K.A., Pyskir, A., Tarasiewicz, P., Kunwar, S., Rohrbach, J., Luo, D., Prasad, M., Fleer, S., Göpfert, J.P., Tandon, A., Mollard, G., Rayaprolu, N., Salathe, M. & Schilling, M. (2020). Deep Learning for Understanding Satellite Imagery: An Experimental Survey. Frontiers in Artificial Intelligence, 3: 534696. Frontiers Media SA. doi: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 (Lecture Notes in Computer Science). In I. Farkaš, P. Masulli & S. Wermter (Hrsg.), Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II (S. 204-213). Gehalten auf der 29th International Conference on Artificial Neural Networks,, Cham: Springer International Publishing. doi: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 (Lecture Notes in Computer Science). In I.V. Tetko, V. Kůrková, P. Karpov & F. Theis (Hrsg.), 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 (S. 302-311). Cham: Springer International Publishing. doi:10.1007/978-3-030-30487-4_24.

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• [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

  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. Elsevier. doi:10.1016/j.cag.2018.08.003.

  PUB | DOI | Download (ext.) | WoS
   
• [6]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316

  Göpfert, J.P., Hammer, B. & Wersing, H. (2018). Mitigating Concept Drift via Rejection (Lecture Notes in Computer Science). In V. Kurkova, Y. Manolopoulos, B. Hammer, L. Iliadis & I. Maglogiannis (Hrsg.), Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Gehalten auf der International Conference on Artificial Neural Networks, Cham: Springer. doi:10.1007/978-3-030-01418-6_45.

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• [5]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273

  Göpfert, C., Pfannschmidt, L., Göpfert, J.P. & Hammer, B. (2018). Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, 69-79. Elsevier. doi:10.1016/j.neucom.2017.11.074.

  PUB | PDF | DOI | WoS
   
• [4]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752

  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). Gehalten auf der IEEE Symposium Series on Computational Intelligence, Piscataway, NJ: IEEE. doi:10.1109/SSCI.2017.8285305.

  PUB | PDF | DOI
   
• [3]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274

  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. Gehalten auf der NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.

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• [2]
  

  2015 | Report | PUB-ID: 2712107

  Stöckel, A., Paaßen, B., Dickfelder, R., Göpfert, J.P., Brazda, N., Müller, H.W., Cimiano, P., Hartung, M. & Klinger, R. (2015). SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. bioRxive.org. doi:10.1101/013458.

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• [1]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760

  Paaßen, B., Stöckel, A., Dickfelder, R., Göpfert, J.P., Brazda, N., Kirchhoffer, T., Müller, H.W., Klinger, R., Hartung, M. & Cimiano, P. (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 (Hrsg.), Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING) (S. 25-32). Dublin, Ireland.

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