PUB - Publikationen an der Universität Bielefeld

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• [6]
  

  2025 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001606

  Feldhans, R., & Hammer, B. (2025). Towards Reliable Drift Detection and Explanation in Text Data. Intelligent Data Engineering and Automated Learning – IDEAL 2024, PT I, Lecture Notes in Computer Science, 15346, 301-312. Cham: Springer . https://doi.org/10.1007/978-3-031-77731-8_28

  PUB | DOI | WoS
   
• [5]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684

  Kummert, J., Schulz, A., Feldhans, R., Habigt, M., Stemmler, M., Kohler, C., Abel, D., et al. (2023). Generating Cardiovascular Data to Improve Training of Assistive Heart Devices. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1292-1297. IEEE. https://doi.org/10.1109/SSCI52147.2023.10372030

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• [4]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683

  Feldhans, R., Schulz, A., Kummert, J., Habigt, M., Stemmler, M., Kohler, C., Abel, D., et al. (2023). Data Augmentation for Cardiovascular Time Series Data Using WaveNet. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 836-841. IEEE. https://doi.org/10.1109/SSCI52147.2023.10371813

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• [3]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457

  Schroeder, S., Schulz, A., Tarakanov, I., Feldhans, R., & Hammer, B. (2023). Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 134-145). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_11

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• [2]
  

  2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746

  Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2022). Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters, 55, 5273–5297. https://doi.org/10.1007/s11063-022-10826-5

  PUB | PDF | DOI | Download (ext.) | WoS
   
• [1]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373

  Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2021). Contrastive Explanations for Explaining Model Adaptations. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I (pp. 101-112). Cham: Springer . https://doi.org/10.1007/978-3-030-85030-2_9

  PUB | DOI