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.

  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: Hujun Yin; David Camacho; Peter Tino (Hrsg.): Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Cham: Springer International Publishing. (Lecture Notes in Computer Science, 13756). S. 313-325.

  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.

  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: Igor Farkaš; Paolo Masulli; Sebastian Otte; Stefan 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. Cham: Springer International Publishing. (Lecture Notes in Computer Science, 12891). S. 29-41.

  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.

  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.

  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

  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

  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: Igor Farkaš; Paolo Masulli; Stefan 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. Cham: Springer International Publishing. (Lecture Notes in Computer Science, 12397). S. 204-213.

  PUB | DOI
   
• [10]
  

  2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085

  Göpfert, J. P.; Wersing, H.; Hammer, B. (2019): Recovering Localized Adversarial Attacks. In: Igor V. Tetko; Věra Kůrková; Pavel Karpov; Fabian 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. Cham: Springer International Publishing. (Lecture Notes in Computer Science, ). S. 302-311.

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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

  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.

  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.

  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. In: Vera Kurkova; Yannis Manolopoulos; Barbara Hammer; Lazaros Iliadis; Ilias Maglogiannis (Hrsg.): Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Cham: Springer. (Lecture Notes in Computer Science, 11139).

  PUB | PDF | DOI
   
• [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.

  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. In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.

  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. In: Proceedings of the 2017 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.

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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: Diana Maynard; Marieke Erp van; Brian Davis (Hrsg.): Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING). Dublin, Ireland. S. 25-32.

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