52 Publikationen
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2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573Grimmelsmann, N., Mechtenberg, M., Vieth, M., Schulz, A., Hammer, B., & 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. Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, 611-621. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012368700003657
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2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572Schroeder, S., Schulz, A., Hinder, F., & Hammer, B. (2024). Semantic Properties of Cosine Based Bias Scores for Word Embeddings. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, 160-168. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012577200003654
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2977923Nelkner, J., Huang, L., Lin, T. W., Schulz, A., Osterholz, B., Henke, C., Blom, J., et al. (2023). Abundance, classification and genetic potential of Thaumarchaeota in metagenomes of European agricultural soils: a meta-analysis. Environmental Microbiome, 18(1), 26. https://doi.org/10.1186/s40793-023-00479-9
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684Kummert, 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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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683Feldhans, 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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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457Schroeder, 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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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455Liuliakov, A., Schulz, A., Hermes, L., & Hammer, B. (2023). One-Class Intrusion Detection with Dynamic Graphs. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds.), Lecture Notes in Computer Science. 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 (pp. 537-549). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44216-2_44
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250Vieth, M., Schulz, A., & Hammer, B. (2023). Extending Drift Detection Methods to Identify When Exactly the Change Happened. 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. 92-104). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_8
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429Kummert, J., Schulz, A., & Hammer, B. (2023). Metric Learning with Self-Adjusting Memory for Explaining Feature Drift. SN Computer Science, 4(4), 376. https://doi.org/10.1007/s42979-023-01782-5
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383Artelt, A., Schulz, A., & Hammer, B. (2023). "Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 27-38. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011618300003411
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381Schroeder, S., Schulz, A., Kenneweg, P., & Hammer, B. (2023). So Can We Use Intrinsic Bias Measures or Not? Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 403-410. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011693700003411
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382Kenneweg, P., Schroeder, S., Schulz, A., & Hammer, B. (2023). Debiasing Sentence Embedders Through Contrastive Word Pairs. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 205-212. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011615300003411
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2022 | Zeitschriftenaufsatz | PUB-ID: 2978998Paaßen, B., Schulz, A., C. Stewart, T., & Hammer, B. (2022). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2575–2585. https://doi.org/10.1109/TNNLS.2021.3094139
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096Kenneweg, P., Schulz, A., Schroeder, S., & Hammer, B. (2022). Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 252-261). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_25
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2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829Langnickel, L., Schulz, A., Hammer, B., & Fluck, J. (2022). BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models. arXiv. https://doi.org/10.48550/ARXIV.2202.10101
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542Paaßen, B., Schulz, A., & Hammer, B. (2021). Reservoir Stack Machines. Neurocomputing, 470, 352-364. https://doi.org/10.1016/j.neucom.2021.05.106
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2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229Paassen, B., Schulz, A., Stewart, T. C., & Hammer, B. (2021). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 1-11. https://doi.org/10.1109/TNNLS.2021.3094139
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2952937Kummert, J., Schulz, A., Redick, T., Ayoub, N., Modabber, A., Abel, D., & Hammer, B. (2021). Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors, 21(6), 2114. https://doi.org/10.3390/s21062114
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2020 | Konferenzbeitrag | PUB-ID: 2943260Schulz, A., Hinder, F., & Hammer, B. (2020). DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. https://doi.org/10.24963/ijcai.2020/319
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931Paaßen, B., & Schulz, A. (2020). Reservoir memory machines. In M. Verleysen (Ed.), Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020) (pp. 567-572). Bruges: i6doc.
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2020 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2952742Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M., Lux, M., Schulz, A., et al. (2020). The composition of the human ribosome varies significantly in different normal and malignant tissues. Proceedings: AACR Annual Meeting 2020, Cancer Research, 80 Philadelphia: Amer Assoc Cancer Research. https://doi.org/10.1158/1538-7445.AM2020-5865
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2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2944327Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M., Lux, M., Schulz, A., 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. doi:10.1093/nar/gkaa485
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2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458Prahm, C., Schulz, A., Paaßen, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., Hammer, B., et al. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/TNSRE.2019.2907200
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2018 | Konferenzbeitrag | PUB-ID: 2930001Schulz, A., Queißer, J., Ishihara, H., & 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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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2018). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, 122-133. doi:10.1016/j.neucom.2017.11.072
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2017 | Datenpublikation | PUB-ID: 2912671Paaßen, B., & Schulz, A. (2017). Linear Supervised Transfer Learning Toolbox. Bielefeld University. doi:10.4119/unibi/2912671
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2017). An EM transfer learning algorithm with applications in bionic hand prostheses. In M. Verleysen (Ed.), Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017) (pp. 129-134). Bruges: i6doc.com.
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037Prahm, C., Schulz, A., Paaßen, B., Aszmann, O., Hammer, B., & Dorffner, G. (2017). Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In A. ten Telje, C. Popow, J. H. Holmes, & L. Sacchi (Eds.), Lecture Notes in Computer Science: Vol. 10259. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017) (pp. 338--342). Springer. doi:10.1007/978-3-319-59758-4_40
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2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909Schulz, A., & Hammer, B. (2016). Discriminative Dimensionality Reduction in Kernel Space. ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855Paaßen, B., Schulz, A., & Hammer, B. (2016). Linear Supervised Transfer Learning for Generalized Matrix LVQ. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation 2016 (pp. 11-18).
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178Prahm, C., Paaßen, B., Schulz, A., Hammer, B., & Aszmann, O. (2016). Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In J. Ibáñez, J. Gonzáles-Vargas, J. M. Azorín, M. Akay, & J. L. Pons (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016) (pp. 153--157). Springer. doi:10.1007/978-3-319-46669-9_28
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2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303Schulz, A., & Hammer, B. (2015). Visualization of Regression Models Using Discriminative Dimensionality Reduction. Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, 9257, 437-449. Cham: Springer Science + Business Media. doi:10.1007/978-3-319-23117-4_38
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2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325Blöbaum, P., Schulz, A., & Hammer, B. (2015). Unsupervised Dimensionality Reduction for Transfer Learning. In M. Verleysen (Ed.), Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 507-512). Louvain-la-Neuve: Ciaco.
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2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369Mokbel, B., & Schulz, A. (2015). Towards Dimensionality Reduction for Smart Home Sensor Data. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015) (pp. 41-48).
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320Frenay, B., Hofmann, D., Schulz, A., Biehl, M., & Hammer, B. (2014). Valid interpretation of feature relevance for linear data mappings. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 149-156. Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE). https://doi.org/10.1109/cidm.2014.7008661
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557Schulz, A., Gisbrecht, A., & Hammer, B. (2014). Relevance learning for dimensionality reduction. In M. Verleysen (Ed.), ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 165-170). Bruges, Belgium: i6doc.com.
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322Bloebaum, P., & Schulz, A. (2014). Transfer Learning without given Correspondences. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014) (pp. 42-51).
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2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324Gisbrecht, A., Schulz, A., & Hammer, B. (2014). Discriminative Dimensionality Reduction for the Visualization of Classifiers. Pattern Recognition Applications and Methods, Advances in Intelligent Systems and Computing, 318, 39-56. Cham: Springer Science + Business Media. doi:10.1007/978-3-319-12610-4_3
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2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454Hammer, B., Gisbrecht, A., & Schulz, A. (2013). Applications of discriminative dimensionality reduction. Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 33-41. SCITEPRESS. doi:10.5220/0004245300330041
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2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456Schulz, A., Gisbrecht, A., & Hammer, B. (2013). Using Nonlinear Dimensionality Reduction to Visualize Classifiers. In I. Rojas, G. Joya, & J. Gabestany (Eds.), Lecture Notes in Computer Science: Vol. 7902. Advances in computational intelligence. Proceedings. Vol 1 (pp. 59-68). Springer. doi:10.1007/978-3-642-38679-4_4
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2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622466Vukanovicz, S., Schulz, A., Haschke, R., & Ritter, H. (2013). Learning the Appropriate Model Population Structures for Locally Weighted Regression. Workshop New Challenges in Neural Computation 2013, Machine Learning Reports, 2013, 87. Bielefeld: Universität Bielefeld.
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2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467Schulz, A., Gisbrecht, A., & Hammer, B. (2013). Classifier inspection based on different discriminative dimensionality reductions. Workshop NC^2 2013, 77-86
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2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449Schulz, A., Gisbrecht, A., Bunte, K., & Hammer, B. (2012). How to visualize a classifier? Proceedings of the Workshop - New Challenges in Neural Computation 2012, 73-83
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