52 Publikationen

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  • [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: 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: 160-168.
    PUB | DOI
     
  • [50]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2977923 OA
    Nelkner J, Huang L, Lin TW, 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: 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: 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: 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, 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, 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, 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, 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, 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, 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: 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: 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: 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: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 252-261.
    PUB | DOI
     
  • [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 TC, 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: 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, 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. Cancer Research, 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, 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.
    Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press).
    PUB | PDF
     
  • [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.
    Presented at the SSCI CIDM 2017.
    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: 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, 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: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 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.
    PUB | PDF
     
  • [20]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen B, Schulz A, 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, 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, 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 JM, Akay M, Pons JL (Eds); Springer: 153--157.
    PUB | PDF | DOI | Download (ext.)
     
  • [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.
    PUB | PDF | DOI | WoS
     
  • [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.
    PUB | PDF | DOI
     
  • [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.
    PUB | PDF | DOI | WoS
     
  • [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. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.
    PUB | PDF | DOI
     
  • [14]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
    Blöbaum P, Schulz A, 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.
    PUB | PDF
     
  • [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): 1-8.
    PUB | DOI
     
  • [12]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369 OA
    Mokbel B, 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, Villmann T (Eds); Machine Learning Reports, 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: 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): 149-156.
    PUB | PDF | DOI
     
  • [9]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
    Schulz A, Gisbrecht A, 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.
    PUB
     
  • [8]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322
    Bloebaum P, Schulz A (2014)
    Transfer Learning without given Correspondences.
    In: Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014). Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 42-51.
    PUB
     
  • [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. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.
    PUB | DOI
     
  • [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: 33-41.
    PUB | DOI
     
  • [5]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
    Schulz A, Gisbrecht A, Hammer B (2013)
    Using Nonlinear Dimensionality Reduction to Visualize Classifiers.
    In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 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. Machine Learning Reports, 2013. Bielefeld: Universität Bielefeld: 87.
    PUB
     
  • [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: 77-86.
    PUB
     
  • [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: 73-83.
    PUB
     
  • [1]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
    Hammer B, Gisbrecht A, Schulz A (2012)
    How to Visualize Large Data Sets?
    Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.
    PUB | DOI
     

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