Publications
Filtered:
Ordered by year and author
2024
Buchelt, A., Adrowitzer, A., Kieseberg, P., Gollob, C., Nothdurft, A., Eresheim, S., Tschiatschek, S., Stampfer, K., & Holzinger, A. (2024). Exploring artificial intelligence for applications of drones in forest ecology and management. Forest Ecology and Management, 551, 121530. https://doi.org/10.1016/j.foreco.2023.121530
2023
Bruckner, Franziska, Feyersinger, Erwin, & Lechner, Patrik. (2023, June 14). AniVision: Machine Learning as a Tool for Studying Animation in Ephemeral Films [Vortrag]. Society for Animation Studies 34th Annual Conference – The Animated Environment, Online – Glassboro. https://www.sas34.org/
Dumphart, B., Slijepcevic, D., Zeppelzauer, M., Kranzl, A., Unglaube, F., Baca, A., & Horsak, B. (2023). Robust deep learning-based gait event detection across various pathologies. PLOS ONE, 18(8), e0288555. https://doi.org/10.1371/journal.pone.0288555
Dumphart, B., Slijepcevic, D., Kranz, A., Zeppelzauer, M., & Horsak, B. (2023). Is it time to re-think the appropriateness of autocorrelation for gait event detection? Preliminary results of an ongoing study. Gait & Posture, 106, S50–S51. https://doi.org/10.1016/j.gaitpost.2023.07.064
Horst, F., Slijepcevic, D., Simak, M., Horsak, B., Schöllhorn, W. I., & Zeppelzauer, M. (2023). Modeling biological individuality using machine learning: A study on human gait. Computational and Structural Biotechnology Journal, 21, 3414–3423. https://doi.org/10.1016/j.csbj.2023.06.009
Kovac, F. (2023, June 3). [Keynote] Standing Still Is Not An Option: Alternative Baselines for Attainable Utility Preservation. Machine Learning Prague (MLPRAGUE) 2023, Prague, Czech Republic. https://mlprague.com/
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy. IEEE Access, 11, 65906–65923. https://doi.org/10.1109/ACCESS.2023.3289986
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Towards more transparency: The utility of Grad-CAM in tracing back deep learning based classification decisions in children with cerebral palsy. Gait & Posture, 100, 32–33. https://doi.org/10.1016/j.gaitpost.2022.11.045
Slijepcevic, D., Horst, F., Simak, M., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2023). Towards personalized gait rehabilitation: How robustly can we identify personal gait signatures with machine learning? Gait & Posture, 106, S192–S193. https://doi.org/10.1016/j.gaitpost.2023.07.232
2022
Hogan, A., Cochez, M., Melo, G. de, & Neumaier, S. (2022). Knowledge graphs. Morgan & Claypool Publishers.
Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (2022). 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022 Vienna, August 23–26, 2022 Proceedings. Springer. https://link.springer.com/content/pdf/10.1007/978-3-031-14463-9.pdf
Kovac, F., Eigner, O., Adrowitzer, A., Scholnast, H., & Buchelt, A. (2022). Classification of rain events using directional radio data of commercial microwave links. 2022 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 1–6. https://doi.org/10.1109/COINS54846.2022.9855003
Nurgazina, J., Felberbauer, T., Asprion, B., & Pinnamaraju, P. (2022). Visualization and clustering for rolling forecast quality verification: A case study in the automotive industry. Procedia Computer Science, 200, 1048–1057. https://doi.org/https://doi.org/10.1016/j.procs.2022.01.304
Rind, A., Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., & Horsak, B. (2022). Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy. Proc. 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX), 7–15. https://doi.org/10.1109/TREX57753.2022.00006
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/10.1145/3474121
Slijepcevic, D., Horst, F., Simak, M., Lapuschkin, S., Raberger, A. M., Samek, W., Breiteneder, C., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2022). Explaining machine learning models for age classification in human gait analysis. Gait & Posture, 97, S252–S253. https://doi.org/10.1016/j.gaitpost.2022.07.153
2021
Adensamer, A., Gsenger, R., & Klausner, L. D. (2021). “Computer Says No”: Algorithmic Decision Support and Organisational Responsibility. Journal of Responsible Technology, 7–8. https://doi.org/10/gm6t7q
Adensamer, A., & Klausner, L. D. (2021). “Part Man, Part Machine, All Cop”: Automation in Policing. Frontiers in Artificial Intelligence, 2021(4). https://doi.org/10/gk3q27
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
Dumphart, B., Slijepčević, D., Unglaube, F., Kranzl, A., Baca, A., Zeppelzauer, M., & Horsak, B. (2021). An automated deep learning-based gait event detection algorithm for various pathologies. Gait & Posture, 90, 50–51. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.026