52 Publikationen

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  • [52]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    Grimmelsmann, N., Mechtenberg, M., Vieth, M., Schulz, A., Hammer, B., and Schneider, A. (2024). “Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks” in Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (SCITEPRESS - Science and Technology Publications), 611-621.
    PUB | DOI
     
  • [51]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    Schroeder, S., Schulz, A., Hinder, F., and Hammer, B. (2024). “Semantic Properties of Cosine Based Bias Scores for Word Embeddings” in Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (SCITEPRESS - Science and Technology Publications), 160-168.
    PUB | DOI
     
  • [50]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2977923 OA
    Nelkner, J., Huang, L., Lin, T. W., Schulz, A., Osterholz, B., Henke, C., Blom, J., Pühler, A., Sczyrba, A., and Schlüter, A. (2023). Abundance, classification and genetic potential of Thaumarchaeota in metagenomes of European agricultural soils: a meta-analysis. Environmental Microbiome 18:26.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [49]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684
    Kummert, J., Schulz, A., Feldhans, R., Habigt, M., Stemmler, M., Kohler, C., Abel, D., Rossaint, R., and Hammer, B. (2023). “Generating Cardiovascular Data to Improve Training of Assistive Heart Devices” in 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE), 1292-1297.
    PUB | DOI
     
  • [48]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683
    Feldhans, R., Schulz, A., Kummert, J., Habigt, M., Stemmler, M., Kohler, C., Abel, D., Rossaint, R., and Hammer, B. (2023). “Data Augmentation for Cardiovascular Time Series Data Using WaveNet” in 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE), 836-841.
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  • [47]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457
    Schroeder, S., Schulz, A., Tarakanov, I., Feldhans, R., and Hammer, B. (2023). “Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation” in Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, Rojas, I., Joya, G., and Catala, A. eds. Lecture Notes in Computer Science (Cham: Springer Nature Switzerland), 134-145.
    PUB | DOI
     
  • [46]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    Liuliakov, A., Schulz, A., Hermes, L., and Hammer, B. (2023). “One-Class Intrusion Detection with Dynamic Graphs” in Artificial Neural Networks and Machine Learning – ICANN 2023. 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part IV, Iliadis, L., Papaleonidas, A., Angelov, P., and Jayne, C. eds. Lecture Notes in Computer Science (Cham: Springer Nature Switzerland), 537-549.
    PUB | DOI
     
  • [45]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250
    Vieth, M., Schulz, A., and Hammer, B. (2023). “Extending Drift Detection Methods to Identify When Exactly the Change Happened” in Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, Rojas, I., Joya, G., and Catala, A. eds. Lecture Notes in Computer Science (Cham: Springer Nature Switzerland), 92-104.
    PUB | DOI
     
  • [44]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429
    Kummert, J., Schulz, A., and Hammer, B. (2023). Metric Learning with Self-Adjusting Memory for Explaining Feature Drift. SN Computer Science 4:376.
    PUB | DOI
     
  • [43]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383
    Artelt, A., Schulz, A., and Hammer, B. (2023). “"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction” in Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (Setúbal, Portugal: SCITEPRESS - Science and Technology Publications), 27-38.
    PUB | DOI | arXiv
     
  • [42]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    Schroeder, S., Schulz, A., Kenneweg, P., and Hammer, B. (2023). “So Can We Use Intrinsic Bias Measures or Not?” in Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (Setúbal, Portugal: SCITEPRESS - Science and Technology Publications), 403-410.
    PUB | DOI
     
  • [41]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    Kenneweg, P., Schroeder, S., Schulz, A., and Hammer, B. (2023). “Debiasing Sentence Embedders Through Contrastive Word Pairs” in Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (Setúbal, Portugal: SCITEPRESS - Science and Technology Publications), 205-212.
    PUB | DOI
     
  • [40]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, B., Schulz, A., C. Stewart, T., and Hammer, B. (2022). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems 33, 2575–2585.
    PUB | DOI | Download (ext.) | arXiv
     
  • [39]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    Kenneweg, P., Schulz, A., Schroeder, S., and Hammer, B. (2022). “Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers” in Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Yin, H., Camacho, D., and Tino, P. eds. Lecture Notes in Computer Science, vol. 13756, (Cham: Springer International Publishing), 252-261.
    PUB | DOI
     
  • [38]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    Langnickel, L., Schulz, A., Hammer, B., and Fluck, J. (2022). BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models. arXiv.
    PUB | DOI | arXiv
     
  • [37]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen, B., Schulz, A., and Hammer, B. (2021). Reservoir Stack Machines. Neurocomputing 470, 352-364.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [36]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229
    Paassen, B., Schulz, A., Stewart, T. C., and Hammer, B. (2021). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 1-11.
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC | arXiv
     
  • [35]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2952937 OA
    Kummert, J., Schulz, A., Redick, T., Ayoub, N., Modabber, A., Abel, D., and Hammer, B. (2021). Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors 21:2114.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [34]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    Schulz, A., Hinder, F., and Hammer, B. (2020). “DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.
    PUB | DOI | Download (ext.) | arXiv
     
  • [33]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931
    Paaßen, B., and Schulz, A. (2020). “Reservoir memory machines” in Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020), Verleysen, M. ed. (Bruges: i6doc), 567-572.
    PUB | Download (ext.) | arXiv
     
  • [32]
    2020 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2952742
    Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M., Lux, M., Schulz, A., Klecha, T., Doniach, S., Khiabanian, H., et al. (2020). “The composition of the human ribosome varies significantly in different normal and malignant tissues” in Proceedings: AACR Annual Meeting 2020 Cancer Research, vol. 80, (Philadelphia: Amer Assoc Cancer Research).
    PUB | DOI | WoS
     
  • [31]
    2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2944327
    Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M., Lux, M., Schulz, A., Klecha, T., Doniach, S., Khiabanian, H., et al. (2020). Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic acids research.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [30]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
    Prahm, C., Schulz, A., Paaßen, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., Hammer, B., and Aszmann, O. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 956-962.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [29]
    2018 | Konferenzbeitrag | PUB-ID: 2930001 OA
    Schulz, A., Queißer, J., Ishihara, H., and Asada, M. (2018).“Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto”. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press).
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  • [28]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B., Schulz, A., Hahne, J., and Hammer, B. (2018). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing 298, 122-133.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [27]
    2018 | Konferenzbeitrag | PUB-ID: 2916318
    Berger, K., Schulz, A., Paaßen, B., and Hammer, B. (2018).“Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier”. Presented at the SSCI CIDM 2017.
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  • [26]
    2017 | Datenpublikation | PUB-ID: 2912671 OA
    Paaßen, B., and Schulz, A. (2017). Linear Supervised Transfer Learning Toolbox. Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [25]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
    Paaßen, B., Schulz, A., Hahne, J., and Hammer, B. (2017). “An EM transfer learning algorithm with applications in bionic hand prostheses” in Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), Verleysen, M. ed. (Bruges: i6doc.com), 129-134.
    PUB | PDF
     
  • [24]
    2017 | Bielefelder E-Dissertation | PUB-ID: 2914256 OA
    Schulz, A. (2017). Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld.
    PUB | PDF
     
  • [23]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    Schulz, A., Brinkrolf, J., and Hammer, B. (2017). Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing 268, 34-41.
    PUB | PDF | DOI | WoS
     
  • [22]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C., Schulz, A., Paaßen, B., Aszmann, O., Hammer, B., and Dorffner, G. (2017). “Echo State Networks as Novel Approach for Low-Cost Myoelectric Control” in Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017), ten Telje, A., Popow, C., Holmes, J. H., and Sacchi, L. eds. Lecture Notes in Computer Science, vol. 10259, (Springer), 338--342.
    PUB | Dateien verfügbar | DOI
     
  • [21]
    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
    Schulz, A., and Hammer, B. (2016). “Discriminative Dimensionality Reduction in Kernel Space” in ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016 (i6doc.com).
    PUB | PDF
     
  • [20]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen, B., Schulz, A., and Hammer, B. (2016). “Linear Supervised Transfer Learning for Generalized Matrix LVQ” in Proceedings of the Workshop New Challenges in Neural Computation 2016, Hammer, B., Martinetz, T., and Villmann, T. eds. Machine Learning Reports 11-18.
    PUB | Download (ext.)
     
  • [19]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    Prahm, C., Paaßen, B., Schulz, A., Hammer, B., and Aszmann, O. (2016). “Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift” in Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), Ibáñez, J., Gonzáles-Vargas, J., Azorín, J. M., Akay, M., and Pons, J. L. eds. (Springer), 153--157.
    PUB | PDF | DOI | Download (ext.)
     
  • [18]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
    Gisbrecht, A., Schulz, A., and Hammer, B. (2015). Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing 147, 71-82.
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  • [17]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
    Schulz, A., Mokbel, B., Biehl, M., and Hammer, B. (2015). “Inferring Feature Relevances From Metric Learning” in 2015 IEEE Symposium Series on Computational Intelligence (Piscataway, NJ: IEEE).
    PUB | PDF | DOI
     
  • [16]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
    Schulz, A., Gisbrecht, A., and Hammer, B. (2015). Using Discriminative Dimensionality Reduction to Visualize Classifiers. Neural Processing Letters 42, 27-54.
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  • [15]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
    Schulz, A., and Hammer, B. (2015). “Visualization of Regression Models Using Discriminative Dimensionality Reduction” in Computer Analysis of Images and Patterns Lecture Notes in Computer Science, vol. 9257, (Cham: Springer Science + Business Media), 437-449.
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  • [14]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
    Blöbaum, P., Schulz, A., and Hammer, B. (2015). “Unsupervised Dimensionality Reduction for Transfer Learning” in Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Verleysen, M. ed. (Louvain-la-Neuve: Ciaco), 507-512.
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  • [13]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
    Schulz, A., and Hammer, B. (2015). “Discriminative dimensionality reduction for regression problems using the Fisher metric” in 2015 International Joint Conference on Neural Networks (IJCNN) (Institute of Electrical & Electronics Engineers (IEEE), 1-8.
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  • [12]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369 OA
    Mokbel, B., and Schulz, A. (2015). “Towards Dimensionality Reduction for Smart Home Sensor Data” in Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015), Hammer, B., Martinetz, T., and Villmann, T. eds. Machine Learning Reports 41-48.
    PUB | PDF | Download (ext.)
     
  • [11]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
    Schulz, A., and Hammer, B. (2015). “Metric Learning in Dimensionality Reduction” in Proceedings of the International Conference on Pattern Recognition Applications and Methods (Scitepress), 232-239.
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  • [10]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320 OA
    Frenay, B., Hofmann, D., Schulz, A., Biehl, M., and Hammer, B. (2014). “Valid interpretation of feature relevance for linear data mappings” in 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE), 149-156.
    PUB | PDF | DOI
     
  • [9]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
    Schulz, A., Gisbrecht, A., and Hammer, B. (2014). “Relevance learning for dimensionality reduction” in ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Verleysen, M. ed. (Bruges, Belgium: i6doc.com), 165-170.
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  • [8]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322
    Bloebaum, P., and Schulz, A. (2014). “Transfer Learning without given Correspondences” in Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014), Hammer, B., Martinetz, T., and Villmann, T. eds. Machine Learning Reports 42-51.
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  • [7]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
    Gisbrecht, A., Schulz, A., and Hammer, B. (2014). “Discriminative Dimensionality Reduction for the Visualization of Classifiers” in Pattern Recognition Applications and Methods Advances in Intelligent Systems and Computing, vol. 318, (Cham: Springer Science + Business Media), 39-56.
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  • [6]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
    Hammer, B., Gisbrecht, A., and Schulz, A. (2013). “Applications of discriminative dimensionality reduction” in Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods (SCITEPRESS), 33-41.
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  • [5]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
    Schulz, A., Gisbrecht, A., and Hammer, B. (2013). “Using Nonlinear Dimensionality Reduction to Visualize Classifiers” in Advances in computational intelligence. Proceedings. Vol 1, Rojas, I., Joya, G., and Gabestany, J. eds. Lecture Notes in Computer Science, vol. 7902, (Springer), 59-68.
    PUB | DOI | WoS
     
  • [4]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622466
    Vukanovicz, S., Schulz, A., Haschke, R., and Ritter, H. (2013). “Learning the Appropriate Model Population Structures for Locally Weighted Regression” in Workshop New Challenges in Neural Computation 2013 Machine Learning Reports, vol. 2013, (Bielefeld: Universität Bielefeld), 87.
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  • [3]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
    Schulz, A., Gisbrecht, A., and Hammer, B. (2013). “Classifier inspection based on different discriminative dimensionality reductions” in Workshop NC^2 2013 (TR Machine Learning Reports), 77-86.
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  • [2]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
    Schulz, A., Gisbrecht, A., Bunte, K., and Hammer, B. (2012). “How to visualize a classifier?” in Proceedings of the Workshop - New Challenges in Neural Computation 2012 (Machine Learning Reports), 73-83.
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  • [1]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
    Hammer, B., Gisbrecht, A., and Schulz, A. (2012).“How to Visualize Large Data Sets?”. Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.
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