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2020 – today
- 2024
[j8]Felix Tempel
, Espen Alexander Fürst Ihlen, Inga Strümke:
AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search. IEEE Access 12: 39505-39516 (2024)- [j7]Kimji N. Pellano, Inga Strümke, Espen A. F. Ihlen:
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition. Sensors 24(6): 1940 (2024) - [i22]Felix Tempel, Inga Strümke, Espen Alexander Fürst Ihlen:
AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search. CoRR abs/2402.01313 (2024) - [i21]Kimji N. Pellano, Inga Strümke, Espen Alexander Fürst Ihlen:
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition. CoRR abs/2402.12790 (2024) - [i20]Kimji N. Pellano, Inga Strümke, Daniel Groos, Lars Adde, Espen Alexander Fürst Ihlen:
Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy. CoRR abs/2409.00001 (2024) - [i19]Felix Tempel, Espen A. F. Ihlen, Inga Strümke:
Lightweight Neural Architecture Search for Cerebral Palsy Detection. CoRR abs/2409.20060 (2024) - [i18]Marte Eggen, Inga Strümke:
A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze. CoRR abs/2410.12820 (2024) - 2023
- [j6]Vilde B. Gjærum, Inga Strümke, Jakob Løver, Timothy Miller, Anastasios M. Lekkas:
Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications. Neurocomputing 515: 133-144 (2023) - [j5]Pål Vegard Johnsen, Inga Strümke, Mette Langaas, Andrew Thomas DeWan, Signe Riemer-Sørensen:
Inferring feature importance with uncertainties with application to large genotype data. PLoS Comput. Biol. 19(3) (2023) - [c6]Andrea M. Storås, Morten Magnø, Fredrik A. Fineide, Bernd Thiede, Xiangjun Chen, Inga Strümke, Pål Halvorsen, Tor P. Utheim, Michael A. Riegler:
Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence. CBMS 2023: 204-209 - [i17]Inga Strümke, Marija Slavkovik, Clemens Stachl:
Against Algorithmic Exploitation of Human Vulnerabilities. CoRR abs/2301.04993 (2023) - [i16]Patrik Hammersborg, Inga Strümke:
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models. CoRR abs/2307.12601 (2023) - [i15]Patrik Hammersborg, Inga Strümke:
Information based explanation methods for deep learning agents - with applications on large open-source chess models. CoRR abs/2309.09702 (2023) - [i14]Inga Strümke, Helge Langseth:
Lecture Notes in Probabilistic Diffusion Models. CoRR abs/2312.10393 (2023) - 2022
- [j4]Inga Strümke, Marija Slavkovik, Vince Istvan Madai:
The social dilemma in artificial intelligence development and why we have to solve it. AI Ethics 2(4): 655-665 (2022) - [c5]Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas:
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning. ACC 2022: 2683-2690 - [c4]Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler, Inga Strümke:
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning. CBMS 2022: 38-43 - [c3]Cise Midoglu, Andrea M. Storås, Saeed Shafiee Sabet, Malek Hammou, Steven Alexander Hicks, Inga Strümke, Michael Alexander Riegler, Carsten Griwodz, Pål Halvorsen:
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework. ISM 2022: 161-162 - [c2]Malek Hammou, Cise Midoglu, Steven Alexander Hicks, Andrea M. Storås, Saeed Shafiee Sabet, Inga Strümke, Michael A. Riegler, Pål Halvorsen:
Huldra: a framework for collecting crowdsourced feedback on multimedia assets. MMSys 2022: 203-209 - [c1]Andrea M. Storås, Inga Strümke, Michael A. Riegler, Pål Halvorsen:
Explainability methods for machine learning systems for multimodal medical datasets: research proposal. MMSys 2022: 347-351 - [i13]Tannista Banerjee, Ayan Paul, Vishak Srikanth, Inga Strümke:
Socioeconomic disparities and COVID-19: the causal connections. CoRR abs/2201.07026 (2022) - [i12]Vilde B. Gjærum, Inga Strümke, Ole Andreas Alsos, Anastasios M. Lekkas:
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization. CoRR abs/2203.00368 (2022) - [i11]Christophe Grojean, Ayan Paul, Zhuoni Qian, Inga Strümke:
Interpretable machine learning in Physics. CoRR abs/2203.08021 (2022) - [i10]Steven Hicks, Andrea M. Storås, Michael Riegler, Cise Midoglu, Malek Hammou, Thomas de Lange, Sravanthi Parasa, Pål Halvorsen, Inga Strümke:
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations. CoRR abs/2204.00617 (2022) - [i9]Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler, Inga Strümke:
Predicting tacrolimus exposure in kidney transplanted patients using machine learning. CoRR abs/2205.07858 (2022) - [i8]Patrik Hammersborg, Inga Strümke:
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts. CoRR abs/2211.05500 (2022) - [i7]Vilde B. Gjærum, Inga Strümke, Anastasios M. Lekkas, Tim Miller:
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs. CoRR abs/2212.04212 (2022) - 2021
- [j3]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Shapley Values for Feature Selection: The Good, the Bad, and the Axioms. IEEE Access 9: 144352-144360 (2021) - [j2]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Model independent feature attributions: Shapley values that uncover non-linear dependencies. PeerJ Comput. Sci. 7: e582 (2021) - [i6]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Shapley values for feature selection: The good, the bad, and the axioms. CoRR abs/2102.10936 (2021) - [i5]Inga Strümke, Marija Slavkovik, Vince I. Madai:
The social dilemma in AI development and why we have to solve it. CoRR abs/2107.12977 (2021) - [i4]Pål Vegard Johnsen, Inga Strümke, Signe Riemer-Sørensen, Andrew Thomas DeWan, Mette Langaas:
Inferring feature importance with uncertainties in high-dimensional data. CoRR abs/2109.00855 (2021) - [i3]Andrea M. Storås, Inga Strümke, Michael A. Riegler, Jakob Grauslund, Hugo Lewi Hammer, Anis Yazidi, Pål Halvorsen, Kjell G. Gundersen, Tor Paaske Utheim, Catherine Jackson:
Artificial Intelligence in Dry Eye Disease. CoRR abs/2109.01658 (2021) - [i2]Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas:
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning. CoRR abs/2111.02936 (2021) - 2020
- [j1]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Shapley Value Confidence Intervals for Attributing Variance Explained. Frontiers Appl. Math. Stat. 6: 587199 (2020) - [i1]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies. CoRR abs/2007.06011 (2020)
Coauthor Index
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last updated on 2024-11-25 22:47 CET by the dblp team
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