Alexander Schulz

aschulz@techfak.uni-bielefeld.de
ORCID logo https://orcid.org/0000-0002-0739-612X

PEVZ-ID
34943216

52 Publikationen

Alle markieren

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