Inga Strümke
Orcid: 0000-0003-1820-6544
According to our database1,
Inga Strümke
authored at least 29 papers
between 2020 and 2024.
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Bibliography
2024
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition.
Sensors, March, 2024
AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search.
CoRR, 2024
IEEE Access, 2024
2023
Inferring feature importance with uncertainties with application to large genotype data.
PLoS Comput. Biol., March, 2023
Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications.
Neurocomputing, 2023
Information based explanation methods for deep learning agents - with applications on large open-source chess models.
CoRR, 2023
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models.
CoRR, 2023
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence.
Proceedings of the 36th IEEE International Symposium on Computer-Based Medical Systems, 2023
2022
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs.
CoRR, 2022
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts.
CoRR, 2022
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.
CoRR, 2022
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization.
CoRR, 2022
The social dilemma in artificial intelligence development and why we have to solve it.
AI Ethics, 2022
Explainability methods for machine learning systems for multimodal medical datasets: research proposal.
Proceedings of the MMSys '22: 13th ACM Multimedia Systems Conference, Athlone, Ireland, June 14, 2022
Proceedings of the MMSys '22: 13th ACM Multimedia Systems Conference, Athlone, Ireland, June 14, 2022
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework.
Proceedings of the IEEE International Symposium on Multimedia, 2022
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning.
Proceedings of the 35th IEEE International Symposium on Computer-Based Medical Systems, 2022
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning.
Proceedings of the American Control Conference, 2022
2021
Model independent feature attributions: Shapley values that uncover non-linear dependencies.
PeerJ Comput. Sci., 2021
IEEE Access, 2021
2020
Frontiers Appl. Math. Stat., 2020
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies.
CoRR, 2020