#Machine Learning

Durch Machine Learning lernen künstliche Systeme – ähnlich wie Menschen – aus Erfahrung. Das lässt sich in Forschung und Praxis zu verschiedensten Anwendungen einsetzen – zum Beispiel zur Analyse medizinischer Daten oder zur Abwehr von IT-Angriffen.

Projekte

IMREA - Intelligente Multimodale Immobilienanalyse

Multimodale Informationsextraktions- und maschineller Lernverfahren zur Extraktion immobilienbezogener Attribute und Parameter aus heterogenen Eingabedaten

Publikationen

Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breitender, C., Schöllhorn, W., & Zeppelzauer, M. (2022). Explaining Machine Learning Models for Clinical Gait Analysis. ACM Transactions on Computing for Healthcare, 3(2), 14:1–14:27. https://doi.org/10/gnt2s9
Aigner, W. (2021, July 10). Wie können Daten visualisiert werden? [Invited Talk]. Forum Digitalisierung, St. Pölten, Austria.
Krondorfer, P., Slijepčević, D., Unglaube, F., Kranzl, A., Breiteneder, C., Zeppelzauer, M., & Horsak, B. (2021). Deep learning-based similarity retrieval in clinical 3D gait analysis. Gait & Posture, 90, 127–128. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.066
Horsak, B., Simonlehner, M., Schöffer, L., Dumphart, B., Jalaeefar, A., & Husinsky, M. (2021). Overground walking in a fully immersive Virtual Reality: Preliminary results of a comprehensive study on the effects on walking biomechanics. Gait & Posture, 90, 100–101. https://doi.org/https://doi.org/10.3389/fbioe.2021.780314
Horst, F., Slijepcevic, D., Simak, M., & Schöllhorn, W. I. (2021). Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals. Scientific Data, 8(1), 232. https://doi.org/https://doi.org/10.1038/s41597-021-01014-6
Bernard, Jürgen, Hutter, M., Sedlmair, M., Zeppelzauer, Matthias, & Munzner, Tamara. (2021). A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3–4), 1–42. https://doi.org/10/gnt2wf
Koch, D., Despotovic, M., Thaler, S., & Zeppelzauer, M. (2021). Where do University Graduates live? – A Computer Vision Approach using Satellite Images. Applied Intelligence, 51, 8088–8105. https://doi.org/https://doi.org/10.1007/s10489-021-02268-8
Eresheim, S. (2020). Reinforcement Learning for Incident Protection in IT. First Conference on Mathematics of Data Science (MDS20).
Horsak, B., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. Abstractband Des 3. GAMMA Kongress. 3. GAMMA Kongress, München, Deutschland.
Horsak, B., Simonlehner, M., Schöffer, L., Maureder, J., Schwab, C., Raberger, A. M., Zeller, M., & Husinsky, M. (2020). Applicability and usability of an immersive virtual reality-based balance control exergame for prosthetic users: A pilot study with healthy individuals. Gait & Posture. ESMAC 29th Annual Meeting, Virtual Meeting. https://doi.org/10/gnkdht

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