Inga Strümke

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 34

Top Topics

Human Activity Recognition

Machine Learning

Diffusion Models

Top Venues

Publications

Kimji N. Pellano, Inga Strümke, Espen Alexander Fürst Ihlen
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition. CoRR (2024)
Felix Tempel, Inga Strümke, Espen Alexander Fürst Ihlen
AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search. CoRR (2024)
Felix Tempel, Espen Alexander Fürst Ihlen, Inga Strümke
AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search. IEEE Access 12 (2024)
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) (2024)
Patrik Hammersborg, Inga Strümke
Information based explanation methods for deep learning agents - with applications on large open-source chess models. CoRR (2023)
Inga Strümke, Marija Slavkovik, Clemens Stachl
Against Algorithmic Exploitation of Human Vulnerabilities. CoRR (2023)
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)
Inga Strümke, Helge Langseth
Lecture Notes in Probabilistic Diffusion Models. CoRR (2023)
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)
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 (2023)
Patrik Hammersborg, Inga Strümke
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models. CoRR (2023)
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 (2022)
Andrea M. Storås, Inga Strümke, Michael A. Riegler, Pål Halvorsen
Explainability methods for machine learning systems for multimodal medical datasets: research proposal. MMSys (2022)
Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler, Inga Strümke
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning. CBMS (2022)
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) (2022)
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 (2022)
Tannista Banerjee, Ayan Paul, Vishak Srikanth, Inga Strümke
Socioeconomic disparities and COVID-19: the causal connections. CoRR (2022)
Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler, Inga Strümke
Predicting tacrolimus exposure in kidney transplanted patients using machine learning. CoRR (2022)
Inga Strümke, Marija Slavkovik
Explainability for identification of vulnerable groups in machine learning models. CoRR (2022)
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)
Patrik Hammersborg, Inga Strümke
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts. CoRR (2022)
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)
Vilde B. Gjærum, Inga Strümke, Anastasios M. Lekkas, Tim Miller
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs. CoRR (2022)
Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning. ACC (2022)
Christophe Grojean, Ayan Paul, Zhuoni Qian, Inga Strümke
Interpretable machine learning in Physics. CoRR (2022)
Pål Vegard Johnsen, Inga Strümke, Signe Riemer-Sørensen, Andrew Thomas DeWan, Mette Langaas
Inferring feature importance with uncertainties in high-dimensional data. CoRR (2021)
Andrea M. Storås, Inga Strümke, Michael A. Riegler, Jakob Grauslund, Hugo Lewi Hammer, Anis Yazidi, Pål Halvorsen, Kjell G. Gundersen, Tor P. Utheim, Catherine Jackson
Artificial Intelligence in Dry Eye Disease. CoRR (2021)
Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen
Shapley Values for Feature Selection: The Good, the Bad, and the Axioms. IEEE Access 9 (2021)
Inga Strümke, Marija Slavkovik, Vince I. Madai
The social dilemma in AI development and why we have to solve it. CoRR (2021)
Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen
Shapley values for feature selection: The good, the bad, and the axioms. CoRR (2021)
Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen
Model independent feature attributions: Shapley values that uncover non-linear dependencies. PeerJ Comput. Sci. 7 (2021)
Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning. CoRR (2021)
Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies. CoRR (2020)
Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen
Shapley Value Confidence Intervals for Attributing Variance Explained. Frontiers Appl. Math. Stat. 6 (2020)