Research Output per year
Research Output per year
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Dive into the research topics where Laura Rieger is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Research Output
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A simple defense against adversarial attacks on heatmap explanations
Rieger, L. & Hansen, L. K., 2020, Proceedings of 2020 Workshop on Human Interpretability in Machine Learning. 22 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile2 Downloads (Pure) -
Client Adaptation improves Federated Learning with Simulated Non-IID Clients
Rieger, L., Høegh, R. M. T. & Hansen, L. K., 2020, Proceedings of the 2020 International Workshop on Federated Learning for User Privacy and Data Confidentiality. International Machine Learning Society (IMLS), 11 p. (Proceedings of Machine Learning Research, Vol. 108).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile -
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Rieger, L., Singh, C., Murdoch, W. J. & Yu, B., 2020, Proceedings of the 37th International Conference on Machine Learning. International Machine Learning Society (IMLS), p. 8116-8126 (Proceedings of Machine Learning Research, Vol. 119).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile1 Downloads (Pure) -
IROF: a low resource evaluation metric for explanation methods
Rieger, L. & Hansen, L. K., 2020, Proceedings of the Workshop AI for Affordable Healthcare at ICLR 2020. 11 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile -
Interpretability in Intelligent Systems – A New Concept?
Hansen, L. K. & Rieger, L., 1 Jan 2019, Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, p. 41-49 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11700 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
Projects
- 1 Finished
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Explainability of uncertainty for neural networks
Rieger, L., Hansen, L. K., Nielsen, F. Å., Madsen, K. H., Papapetrou, P. & Samek, W.
Technical University of Denmark
01/09/2017 → 30/10/2020
Project: PhD