19 Publikationen
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2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2967296Velioglu, R., Göpfert, J.P., Artelt, A., Hammer, B.: Explainable Artificial Intelligence for Improved Modeling of Processes. In: Yin, H., Camacho, D., and Tino, P. (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. 13756, p. 313-325. Springer International Publishing, Cham (2022).PUB | DOI
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385Risse, N., Göpfert, C., Göpfert, J.P.: How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Farkaš, I., Masulli, P., Otte, S., and Wermter, S. (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. 12891, p. 29-41. Springer International Publishing, Cham (2021).PUB | DOI
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2021 | Zeitschriftenaufsatz | Angenommen | PUB-ID: 2955245Stallmann, D., Göpfert, J.P., Schmitz, J., Grünberger, A., Hammer, B.: Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation. Bioinformatics . (Accepted).PUB | DOI | WoS | PubMed | Europe PMC
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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.: Deep Learning for Understanding Satellite Imagery: An Experimental Survey. Frontiers in Artificial Intelligence. 3, : 534696 (2020).PUB | PDF | DOI | WoS | PubMed | Europe PMC -
2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094Limberg, C., Göpfert, J.P., Wersing, H., Ritter, H.: Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In: Farkaš, I., Masulli, P., and Wermter, S. (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. 12397, p. 204-213. Springer International Publishing, Cham (2020).PUB | DOI
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2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085Göpfert, J.P., Wersing, H., Hammer, B.: Recovering Localized Adversarial Attacks. In: Tetko, I.V., Kůrková, V., Karpov, P., and Theis, F. (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. p. 302-311. Springer International Publishing, Cham (2019).PUB | DOI
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2018 | Datenpublikation | PUB-ID: 2930611
Hülsmann, F., Göpfert, J.P., Hammer, B., Kopp, S., Botsch, M.: 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 (2018).PUB | Dateien verfügbar | DOI
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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2930862Hülsmann, F., Göpfert, J.P., Hammer, B., Kopp, S., Botsch, M.: 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 (2018).PUB | DOI | Download (ext.) | WoS
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2921316
Göpfert, J.P., Hammer, B., Wersing, H.: Mitigating Concept Drift via Rejection. In: Kurkova, V., Manolopoulos, Y., Hammer, B., Iliadis, L., and Maglogiannis, I. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. 11139, Springer, Cham (2018).PUB | PDF | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752
Göpfert, J.P., Göpfert, C., Botsch, M., Hammer, B.: 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). IEEE, Piscataway, NJ (2017).PUB | PDF | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274
Göpfert, C., Göpfert, J.P., Hammer, B.: 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. (2017).PUB | PDF
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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.: SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. bioRxive.org (2015).PUB | PDF | DOI | Download (ext.)
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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.: Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. In: Maynard, D., Erp van, M., and Davis, B. (eds.) Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING). p. 25-32. Dublin, Ireland (2014).PUB | PDF | Download (ext.)