52 Publikationen

Alle markieren

  • [52]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    Grimmelsmann, 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. In: Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS - Science and Technology Publications. S. 611-621.
    PUB | DOI
     
  • [51]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    Schroeder, S.; Schulz, A.; Hinder, F.; 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. S. 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.; Schlüter, A. (2023): Abundance, classification and genetic potential of Thaumarchaeota in metagenomes of European agricultural soils: a meta-analysis Environmental Microbiome,18:(1):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.; Hammer, B. (2023): Generating Cardiovascular Data to Improve Training of Assistive Heart Devices. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. S. 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.; Hammer, B. (2023): Data Augmentation for Cardiovascular Time Series Data Using WaveNet. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. S. 836-841.
    PUB | DOI
     
  • [47]
    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: Ignacio Rojas; Gonzalo Joya; Andreu Catala (Hrsg.): Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Cham: Springer Nature Switzerland. (Lecture Notes in Computer Science, ). S. 134-145.
    PUB | DOI
     
  • [46]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    Liuliakov, A.; Schulz, A.; Hermes, L.; Hammer, B. (2023): One-Class Intrusion Detection with Dynamic Graphs. In: Lazaros Iliadis; Antonios Papaleonidas; Plamen Angelov; Chrisina Jayne (Hrsg.): 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. Cham: Springer Nature Switzerland. (Lecture Notes in Computer Science, ). S. 537-549.
    PUB | DOI
     
  • [45]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250
    Vieth, M.; Schulz, A.; Hammer, B. (2023): Extending Drift Detection Methods to Identify When Exactly the Change Happened. In: Ignacio Rojas; Gonzalo Joya; Andreu Catala (Hrsg.): Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Cham: Springer Nature Switzerland. (Lecture Notes in Computer Science, ). S. 92-104.
    PUB | DOI
     
  • [44]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429
    Kummert, J.; Schulz, A.; Hammer, B. (2023): Metric Learning with Self-Adjusting Memory for Explaining Feature Drift SN Computer Science,4:(4):376
    PUB | DOI
     
  • [43]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383
    Artelt, A.; Schulz, A.; 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. S. 27-38.
    PUB | DOI | arXiv
     
  • [42]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    Schroeder, S.; Schulz, A.; Kenneweg, P.; 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. S. 403-410.
    PUB | DOI
     
  • [41]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    Kenneweg, P.; Schroeder, S.; Schulz, A.; 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. S. 205-212.
    PUB | DOI
     
  • [40]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, 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.
    PUB | DOI | Download (ext.) | arXiv
     
  • [39]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    Kenneweg, P.; Schulz, A.; Schroeder, S.; Hammer, B. (2022): Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers. 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. 252-261.
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  • [38]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    Langnickel, L.; Schulz, A.; Hammer, B.; 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.; 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.; 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.; Hammer, B. (2021): Efficient Reject Options for Particle Filter Object Tracking in Medical Applications Sensors,21:(6):2114
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [34]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    Schulz, A.; Hinder, F.; 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.; Schulz, A. (2020): Reservoir memory machines. In: Michel Verleysen (Hrsg.): Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020). Bruges: i6doc. S. 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.; Ganesan, S.; Tamayo, P.; Bhanot, G. (2020): The composition of the human ribosome varies significantly in different normal and malignant tissues. In: Proceedings: AACR Annual Meeting 2020. Philadelphia: Amer Assoc Cancer Research. (Cancer Research, 80).
    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.; Ganesan, S.; Tamayo, P.; Bhanot, G. (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.; Aszmann, O. (2019): Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning IEEE Transactions on Neural Systems and Rehabilitation Engineering,27:(5): 956-962.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [29]
    2018 | Konferenzbeitrag | PUB-ID: 2930001 OA
    Schulz, A.; Queißer, J.; Ishihara, H.; Asada, M. (2018): Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto.
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  • [28]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B.; Schulz, A.; Hahne, J.; 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.; Hammer, B. (2018): Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier.
    PUB | DOI
     
  • [26]
    2017 | Datenpublikation | PUB-ID: 2912671 OA
    Paaßen, B.; 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.; Hammer, B. (2017): An EM transfer learning algorithm with applications in bionic hand prostheses. In: Michel Verleysen (Hrsg.): Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Bruges: i6doc.com. S. 129-134.
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  • [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.; Hammer, B. (2017): Efficient Kernelization of Discriminative Dimensionality Reduction Neurocomputing,268:(SI): 34-41.
    PUB | PDF | DOI | WoS
     
  • [22]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C.; Schulz, A.; Paaßen, B.; Aszmann, O.; Hammer, B.; Dorffner, G. (2017): Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In: Annette ten Telje; Christian Popow; John H. Holmes; Lucia Sacchi (Hrsg.): Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). Springer. (Lecture Notes in Computer Science, 10259). S. 338--342.
    PUB | Dateien verfügbar | DOI
     
  • [21]
    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
    Schulz, A.; 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.
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  • [20]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen, B.; Schulz, A.; Hammer, B. (2016): Linear Supervised Transfer Learning for Generalized Matrix LVQ. In: Barbara Hammer; Thomas Martinetz; Thomas Villmann (Hrsg.): Proceedings of the Workshop New Challenges in Neural Computation 2016. (Machine Learning Reports, ). S. 11-18.
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  • [19]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    Prahm, C.; Paaßen, B.; Schulz, A.; Hammer, B.; Aszmann, O. (2016): Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In: Jaime Ibáñez; José Gonzáles-Vargas; José María Azorín; Metin Akay; José Luis Pons (Hrsg.): Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Springer. S. 153--157.
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  • [18]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
    Gisbrecht, A.; Schulz, A.; 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.; Hammer, B. (2015): Inferring Feature Relevances From Metric Learning. In: 2015 IEEE Symposium Series on Computational Intelligence. Piscataway, NJ: IEEE.
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  • [16]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
    Schulz, A.; Gisbrecht, A.; Hammer, B. (2015): Using Discriminative Dimensionality Reduction to Visualize Classifiers Neural Processing Letters,42:(1): 27-54.
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  • [15]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
    Schulz, A.; Hammer, B. (2015): Visualization of Regression Models Using Discriminative Dimensionality Reduction. In: Computer Analysis of Images and Patterns. Cham: Springer Science + Business Media. (Lecture Notes in Computer Science, 9257). S. 437-449.
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  • [14]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
    Blöbaum, P.; Schulz, A.; Hammer, B. (2015): Unsupervised Dimensionality Reduction for Transfer Learning. In: Michel Verleysen (Hrsg.): Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco. S. 507-512.
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  • [13]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
    Schulz, A.; 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). S. 1-8.
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  • [12]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369 OA
    Mokbel, B.; Schulz, A. (2015): Towards Dimensionality Reduction for Smart Home Sensor Data. In: Barbara Hammer; Thomas Martinetz; Thomas Villmann (Hrsg.): Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). (Machine Learning Reports, ). S. 41-48.
    PUB | PDF | Download (ext.)
     
  • [11]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
    Schulz, A.; Hammer, B. (2015): Metric Learning in Dimensionality Reduction. In: Proceedings of the International Conference on Pattern Recognition Applications and Methods. Scitepress. S. 232-239.
    PUB | DOI
     
  • [10]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320 OA
    Frenay, B.; Hofmann, D.; Schulz, A.; Biehl, M.; 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). S. 149-156.
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  • [9]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
    Schulz, A.; Gisbrecht, A.; Hammer, B. (2014): Relevance learning for dimensionality reduction. In: Michel Verleysen (Hrsg.): ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com. S. 165-170.
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  • [8]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322
    Bloebaum, P.; Schulz, A. (2014): Transfer Learning without given Correspondences. In: Barbara Hammer; Thomas Martinetz; Thomas Villmann (Hrsg.): Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014). (Machine Learning Reports, ). S. 42-51.
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  • [7]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
    Gisbrecht, A.; Schulz, A.; Hammer, B. (2014): Discriminative Dimensionality Reduction for the Visualization of Classifiers. In: Pattern Recognition Applications and Methods. Cham: Springer Science + Business Media. (Advances in Intelligent Systems and Computing, 318). S. 39-56.
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  • [6]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
    Hammer, B.; Gisbrecht, A.; Schulz, A. (2013): Applications of discriminative dimensionality reduction. In: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. SCITEPRESS. S. 33-41.
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  • [5]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
    Schulz, A.; Gisbrecht, A.; Hammer, B. (2013): Using Nonlinear Dimensionality Reduction to Visualize Classifiers. In: Ignacio Rojas; Gonzalo Joya; Joan Gabestany (Hrsg.): Advances in computational intelligence. Proceedings. Vol 1. Springer. (Lecture Notes in Computer Science, 7902). S. 59-68.
    PUB | DOI | WoS
     
  • [4]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622466
    Vukanovicz, S.; Schulz, A.; Haschke, R.; Ritter, H. (2013): Learning the Appropriate Model Population Structures for Locally Weighted Regression. In: Workshop New Challenges in Neural Computation 2013. Bielefeld: Universität Bielefeld. (Machine Learning Reports, 2013). S. 87.
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  • [3]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
    Schulz, A.; Gisbrecht, A.; Hammer, B. (2013): Classifier inspection based on different discriminative dimensionality reductions. In: Workshop NC^2 2013. TR Machine Learning Reports. S. 77-86.
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  • [2]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
    Schulz, A.; Gisbrecht, A.; Bunte, K.; Hammer, B. (2012): How to visualize a classifier? In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports. S. 73-83.
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  • [1]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
    Hammer, B.; Gisbrecht, A.; Schulz, A. (2012): How to Visualize Large Data Sets?
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