Browsing NTNU Open by Author "Strumke, Inga"
Now showing items 1-17 of 17
-
Beyond cuts in small signal scenarios: Enhanced sneutrino detectability using machine learning
Alvestad, Daniel; Fomin, Nikolai; Kersten, Jörn; Mæland, Steffen; Strumke, Inga (Peer reviewed; Journal article, 2023)We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two ... -
Bias og kvantitativ analyse innen velferd: opphav til skjevheter og relasjon til utfallsrettferdighet
Storås, Andrea; Prabhu, Robindra; Hammer, Hugo Lewi; Strumke, Inga (Peer reviewed; Journal article, 2022)Ifølge Norges nasjonale strategi for kunstig intelligens er offentlig forvaltning og helse blant Norges satsingsområder for bruk av kunstig intelligens. Maskinlæring er en undergruppe av kunstig intelligens med potensial ... -
Causal connections between socioeconomic disparities and COVID-19 in the USA
Banerjee, Tannista; Paul, Ayan; Srikanth, Vishak; Strumke, Inga (Peer reviewed; Journal article, 2022)With the increasing use of machine learning models in computational socioeconomics, the development of methods for explaining these models and understanding the causal connections is gradually gaining importance. In this ... -
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning
Remman, Sindre Benjamin; Strumke, Inga; Lekkas, Anastasios M. (Peer reviewed; Journal article, 2022)We investigate the effect of including application knowledge about a robotic system states’ causal relations when generating explanations of deep neural network policies. To this end, we compare two methods from explainable ... -
Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework
Midoglu, Cise; Storås, Andrea; Sabet, Saeed; Hammou, Malek; Hicks, Steven; Strumke, Inga; Riegler, Michael; Griwodz, Carsten; Halvorsen, Pål (Chapter, 2022)Subjective user studies are important to ensure the fidelity and usability of systems that generate multimedia content. Testing how end-users and domain experts perceive multimedia assets might provide crucial information. ... -
Explaining a deep reinforcement learning docking agent using linear model trees with user adapted visualization
Gjærum, Vilde Benoni; Strumke, Inga; Alsos, Ole Andreas; Lekkas, Anastasios M. (Peer reviewed; Journal article, 2021)Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such ... -
Field Theory at finite Temperature and Density: Applications to Quark Stars
Strumke, Inga (Master thesis, 2012)Abstract:In this thesis we will consider different thermal field theories, and derive an equation of state for deconfined matter.This equation of state will be used in the Tolman-Oppenheimer-Volkoff (TOV)-equations to ... -
Inferring feature importance with uncertainties with application to large genotype data
Johnsen, Pål Vegard; Strumke, Inga; Langaas, Mette; DeWan, Andrew Thomas; Riemer-Sørensen, Signe (Peer reviewed; Journal article, 2023)Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally ... -
On evaluation metrics for medical applications of artificial intelligence
Hicks, Steven A.; Strumke, Inga; Thambawita, Vajira L B; Hammou, Malek; Riegler, Michael Alexander; Halvorsen, Pål (Peer reviewed; Journal article, 2022)Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics ... -
Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning
Storås, Andrea; Åsberg, Anders; Halvorsen, Pål; Riegler, Michael Alexander; Strumke, Inga (Chapter, 2022) -
Research proposal: Explainability methods for machine learning systems for multimodal medical datasets
Storås, Andrea; Strumke, Inga; Riegler, Michael Alexander; Halvorsen, Pål (Chapter, 2022)This paper contains the research proposal of Andrea M. Storås that was presented at the MMSys 2022 doctoral symposium. Machine learning models have the ability to solve medical tasks with a high level of performance, e.g., ... -
To explain or not to explain? Artificial intelligence explainability in clinical decision support systems
Amann, Julia; Vetter, dennis; Blomberg, Stig Nikolaj Fasmer; Christensen, Helle Collatz; Coffee, Megan; Gerke, Sara; Gilbert, Thomas; Hagendorff, Thilo; Holm, Sune; Livne, Michelle; Spezzatti, Andy; Strumke, Inga; Zicari, Roberto V.; Madai, Vince Istvan (Journal article; Peer reviewed, 2022) -
Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Storås, Andrea; Andersen, Ole Emil; Lockhart, Sam; Thielemann, Roman; Gnesin, Filip; Thambawita, Vajira L B; Hicks, Steven; Kanters, Jørgen K.; Strumke, Inga; Halvorsen, Pål; Riegler, Michael (Peer reviewed; Journal article, 2023)Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide ... -
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
Storås, Andrea; Fineide, Fredrik; Magnø, Morten Schjerven; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Galtung, Hilde; Jensen, Janicke Cecilie Liaaen; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ... -
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
Storås, Andrea; Fineide, Fredrik; Magnø, Morten Schjerven; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Galtung, Hilde; Jensen, Janicke Cecilie Liaaen; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ... -
Vacuum free energy, quark condensate shifts and magnetization in three-flavor chiral perturbation theory to O(𝑝6) in a uniform magnetic field
Adhikari, Prabal; Strumke, Inga (Peer reviewed; Journal article, 2023)We study three-flavor QCD in a uniform magnetic field using chiral perturbation theory (χPT). We construct the vacuum free energy density, quark condensate shifts induced by the magnetic field and the renormalized magnetization ... -
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Hicks, Steven; Storås, Andrea; Riegler, Michael; Midoglu, Cise; Hammou, Malek; Lange, Thomas de; Parasa, Sravanthi; Halvorsen, Pål; Strumke, Inga (Journal article; Peer reviewed, 2024)Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a ...