69 Publikationen
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2023 | Preprint | Veröffentlicht | PUB-ID: 2980970J. Strotherm, et al., “Fairness in KI-Systemen”, 2023.PUB | Download (ext.) | arXiv
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2022 | Konferenzbeitrag | PUB-ID: 2979001B. Paaßen, et al., “Sparse Factor Autoencoders for Item Response Theory”, Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), A.I. Cristea, et al., eds., 2022, pp.17–26.PUB | DOI | Download (ext.)
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2022 | Zeitschriftenaufsatz | PUB-ID: 2978970B. Paaßen, I. Koprinska, and K. Yacef, “Recursive Tree Grammar Autoencoders”, Machine Learning, vol. 111, 2022, pp. 3393–3423.PUB | PDF | DOI | Download (ext.)
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2022 | Zeitschriftenaufsatz | PUB-ID: 2979004B. Paaßen, et al., “A conceptual graph-based model of creativity in learning”, Frontiers in Education, vol. 7, 2022.PUB | PDF | DOI | Download (ext.)
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2022 | Konferenzbeitrag | PUB-ID: 2979003B. Paaßen, et al., “Few-shot Keypose Detection for Learning of Psychomotor Skills”, Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2022), K. Asyraaf Mat Sanusi, et al., eds., 2022, pp.22–27.PUB | Download (ext.)
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2022 | Konferenzbeitrag | PUB-ID: 2979002B. Paaßen, et al., “Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings”, Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) Practitioner’s Track, J.A. DeFalco, et al., eds., 2022, pp.132–137.PUB | DOI | Download (ext.)
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2022 | Konferenzbeitrag | PUB-ID: 2979000B. Paaßen, C. Göpfert, and N. Pinkwart, “Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood”, Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), A.I. Cristea, et al., eds., 2022, pp.555–559.PUB | DOI | Download (ext.)
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2022 | Konferenzbeitrag | PUB-ID: 2978999G. Picones, et al., “Combining domain modelling and student modelling techniques in a single pipeline to support task-sequencing”, Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), A.I. Cristea, et al., eds., 2022, pp.217–227.PUB | DOI | Download (ext.)
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2022 | Zeitschriftenaufsatz | PUB-ID: 2978998B. 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
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2978969B. Paaßen, et al., “Mapping Python Programs to Vectors using Recursive Neural Encodings”, Journal of Educational Datamining, vol. 13, 2021, pp. 1–35.PUB | DOI | Download (ext.)
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2021 | Zeitschriftenaufsatz | PUB-ID: 2978997A. Kovalkov, et al., “Automatic Creativity Measurement in Scratch Programs Across Modalities”, IEEE Transactions on Learning Technologies, vol. 14, 2021, pp. 740–753.PUB | DOI | Download (ext.) | arXiv
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2021 | Konferenzbeitrag | PUB-ID: 2978996D. Bacciu, et al., “Deep learning for graphs”, {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)}, M. Verleysen, ed., 2021, pp.89–98.PUB | Download (ext.)
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2021 | Konferenzbeitrag | PUB-ID: 2978995B. Paaßen and M. Kravčík, “Teaching psychomotor skills using machine learning for error detection”, Proceedings of the 1st International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2021), R. Klemke and K. Asyraaf Mat Sanusi, eds., 2021, pp.8–14.PUB | Download (ext.)
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2021 | Konferenzbeitrag | PUB-ID: 2978967B. Paaßen, “An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs”, Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021), N. Reyes, et al., eds., Springer, 2021, pp.364–371.PUB | DOI | Download (ext.) | arXiv
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2021 | Konferenzbeitrag | PUB-ID: 2978966A. Kovalkov, et al., “Modeling Creativity in Visual Programming: From Theory to Practice”, Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021), F. Bouchet, et al., eds., International Educational Datamining Society, 2021.PUB | Download (ext.)
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2021 | Konferenzbeitrag | PUB-ID: 2978965B. Paaßen, et al., “Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks”, Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021), F. Bouchet, et al., eds., International Educational Datamining Society, 2021.PUB | Download (ext.)
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2021 | Konferenzbeitrag | PUB-ID: 2978964J. McBroom, et al., “Progress Networks as a Tool for Analysing Student Programming Difficulties”, Proceedings of the Twenty-Third Australasian Computing Education Conference (ACE '21), C. Szabo and J. Sheard, eds., Association for Computing Machinery, 2021, pp.158–167.PUB | DOI
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542B. Paaßen, A. Schulz, and B. Hammer, “Reservoir Stack Machines”, Neurocomputing, vol. 470, 2021, pp. 352-364.PUB | DOI | Download (ext.) | WoS | arXiv
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2020 | Konferenzbeitrag | PUB-ID: 2978963B. Paaßen, I. Koprinska, and K. Yacef, “Tree Echo State Autoencoders with Grammars”, Proceedings of the 2020 International Joint Conference on Neural Networks ({IJCNN} 2020), A. Roy, ed., 2020, pp.1–8.PUB | DOI | Download (ext.) | arXiv
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931B. 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
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2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2944191T. Morgenroth, M. Stratemeyer, and B. Paaßen, “The Gendered Nature and Malleability of Gamer Stereotypes”, Cyberpsychology, Behavior, and Social Networking, vol. 23, 2020, pp. 557-561.PUB | DOI | WoS | PubMed | Europe PMC
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2019 | Monographie | PUB-ID: 2935200B. Paaßen, A. Artelt, and B. Hammer, Lecture Notes on Applied Optimization, Faculty of Technology, Bielefeld University: 2019.PUB | Dateien verfügbar
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2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458C. 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
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2019 | Datenpublikation | PUB-ID: 2941052B. Paaßen, Python Programming Dataset, Bielefeld University, 2019.PUB | Dateien verfügbar | DOI
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937053B. Paaßen, “Adversarial Edit Attacks for Tree Data”, Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019), H. Yin, D. Camacho, and P. Tino, eds., Lecture Notes in Computer Science, vol. 11871, Cham: Springer, 2019, pp.359-366.PUB | DOI | Download (ext.) | arXiv
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933502B. Paaßen, et al., “Dynamic fairness - Breaking vicious cycles in automatic decision making”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., Louvain-la-Neuve: i6doc, 2019, pp.477-482.PUB | Download (ext.) | arXiv
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2934571B. Paaßen, et al., “Embeddings and Representation Learning for Structured Data”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., 2019, pp.85-94.PUB | Download (ext.) | arXiv
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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900B. Paaßen, C. Göpfert, and B. Hammer, “Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces”, Neural Processing Letters, vol. 48, 2018, pp. 669-689.PUB | DOI | Download (ext.) | WoS | arXiv
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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505B. 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
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2018 | Datenpublikation | PUB-ID: 2916863B. Paaßen and A. Ahmaro, VBB Shortest Path Data 2018, Bielefeld University, 2018.PUB | Dateien verfügbar | DOI
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2018 | Datenpublikation | PUB-ID: 2919994B. Paaßen, Tree Edit Distance Learning via Adaptive Symbol Embeddings, Bielefeld University, 2018.PUB | Dateien verfügbar | DOI
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2018 | Datenpublikation | PUB-ID: 2916990B. Paaßen, Median Generalized Learning Vector Quantization for Distance Data, Bielefeld University, 2018.PUB | Dateien verfügbar | DOI
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2018 | Datenpublikation | PUB-ID: 2916980B. Paaßen, Relational Neural Gas, Bielefeld University, 2018.PUB | Dateien verfügbar | DOI
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2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389B. Paaßen, et al., “The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces”, Journal of Educational Data Mining, vol. 10, 2018, pp. 1-35.PUB | Download (ext.) | arXiv
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844B. Paaßen, et al., “Tree Edit Distance Learning via Adaptive Symbol Embeddings”, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), J. Dy and A. Krause, eds., Proceedings of Machine Learning Research, vol. 80, 2018, pp.3973-3982.PUB | Download (ext.) | arXiv
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2018 | Preprint | Entwurf | PUB-ID: 2919918B. Paaßen, “Revisiting the tree edit distance and its backtracing: A tutorial”, arXiv:1805.06869, Draft.PUB | Download (ext.) | arXiv
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2017 | Datenpublikation | PUB-ID: 2913104B. Paaßen, Time Series Prediction for Relational and Kernel Data, Bielefeld University, 2017.PUB | Dateien verfügbar | DOI
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2017 | Datenpublikation | PUB-ID: 2912671B. Paaßen and A. Schulz, Linear Supervised Transfer Learning Toolbox, Bielefeld University, 2017.PUB | Dateien verfügbar | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369B. 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
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2017 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2914663B. Paaßen, “Two or three things we do (not) know about distances”, Proceedings of the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017), F.-M. Schleif and T. Villmann, eds., Machine Learning Reports, 2017, pp.32-33.PUB | PDF | Download (ext.)
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2017 | Datenpublikation | PUB-ID: 2913083B. Paaßen, BinaryAdder UML Dataset, Bielefeld University, 2017.PUB | Dateien verfügbar | DOI
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2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037C. 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
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367J. Kummert, et al., “Local Reject Option for Deterministic Multi-class SVM”, Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II, A. E.P. Villa, P. Masulli, and A.J. Pons Rivero, eds., Lecture Notes in Computer Science, vol. 9887, Cham: Springer Nature, 2016, pp.251--258.PUB | DOI
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676B. Paaßen, C. Göpfert, and B. Hammer, “Gaussian process prediction for time series of structured data”, Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco - i6doc.com, 2016, pp.41--46.PUB | PDF
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2016 | Datenpublikation | PUB-ID: 2900684B. Paaßen, Java Sorting Programs, Bielefeld University, 2016.PUB | Dateien verfügbar | DOI
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509B. Paaßen, J. Jensen, and B. Hammer, “Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming”, Proceedings of the 9th International Conference on Educational Data Mining, T. Barnes, M. Chi, and M. Feng, eds., Raleigh, North Carolina, USA: International Educational Datamining Society, 2016, pp.183-190.PUB | Download (ext.)
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2016 | Datenpublikation | PUB-ID: 2900666B. Paaßen, MiniPalindrome, Bielefeld University, 2016.PUB | Dateien verfügbar | DOI
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729C. Göpfert, B. Paaßen, and B. Hammer, “Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning”, Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II, A. E.P. Villa, P. Masulli, and A.J. Pons Rivero, eds., Lecture Notes in Computer Science, vol. 9887, Cham: Springer Nature, 2016, pp.510-517.PUB | PDF | DOI
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855B. 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.)
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178C. 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.)
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2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031B. Mokbel, et al., “Metric learning for sequences in relational LVQ”, Neurocomputing, vol. 169, 2015, pp. 306-322.PUB | PDF | DOI | Download (ext.) | WoS
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2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156B. Paaßen, B. Mokbel, and B. Hammer, “Adaptive structure metrics for automated feedback provision in Java programming”, Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., 2015, pp.307-312.PUB | PDF
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2015 | Report | PUB-ID: 2712107A. Stöckel, et al., SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit, bioRxive.org: 2015.PUB | PDF | DOI | Download (ext.)
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2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087B. Paaßen, B. Mokbel, and B. Hammer, “A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems”, Proceedings of the 8th International Conference on Educational Data Mining, O.C. Santos, et al., eds., International Educational Datamining Society, 2015, pp.632-632.PUB | Download (ext.)
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2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955O. Walter, et al., “Autonomous Learning of Representations”, KI - Künstliche Intelligenz, vol. 29, 2015, pp. 339–351.PUB | PDF | DOI | Download (ext.) | WoS
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2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214D. Hofmann, et al., “Learning interpretable kernelized prototype-based models”, Neurocomputing, vol. 141, 2014, pp. 84-96.PUB | DOI | Download (ext.) | WoS
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554B. Mokbel, B. Paaßen, and B. Hammer, “Adaptive distance measures for sequential data”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.265-270.PUB | PDF
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760B. Paaßen, et al., “Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments”, Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING), D. Maynard, M. Erp van, and B. Davis, eds., Dublin, Ireland: 2014, pp.25-32.PUB | PDF | Download (ext.)
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2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058S. Gross, et al., “Example-based feedback provision using structured solution spaces”, International Journal of Learning Technology, vol. 9, 2014, pp. 248-280.PUB | DOI | Download (ext.)
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067B. Mokbel, B. Paaßen, and B. Hammer, “Efficient Adaptation of Structure Metrics in Prototype-Based Classification”, Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, S. Wermter, et al., eds., Lecture Notes in Computer Science, vol. 8681, Springer, 2014, pp.571-578.PUB | PDF | DOI | Download (ext.)
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2013 | Datenpublikation | PUB-ID: 2692491B. Paaßen, VBB Midi Dataset, Bielefeld University, 2013.PUB | Dateien verfügbar | DOI
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2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185B. Mokbel, et al., “Domain-Independent Proximity Measures in Intelligent Tutoring Systems”, Proceedings of the 6th International Conference on Educational Data Mining (EDM), S.K. D'Mello, R.A. Calvo, and A. Olney, eds., 2013, pp.334-335.PUB | Download (ext.)