Browsing NTNU Open by Author "Riegler, Michael"

Now showing items 1-7 of 7

    • Approximate Bayesian Inference Based on Expected Evaluations 

      Hammer, Hugo Lewi; Riegler, Michael; Tjelmeland, Håkon (Peer reviewed; Journal article, 2023)
      Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing ...

    • A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering 

      Thunold, Håvard Horgen; Riegler, Michael; Yazidi, Anis; Hammer, Hugo Lewi (Journal article; Peer reviewed, 2023)
      An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data ...

    • 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. ...

    • Predicting an unstable tear film through artificial intelligence 

      Fineide, Fredrik; Storås, Andrea; Chen, Xiangjun; Magnø, Morten Schjerven; Yazidi, Anis; Riegler, Michael; Utheim, Tor Paaske (Peer reviewed; Journal article, 2022)
      Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. ...

    • Towards the Neuroevolution of Low-level artificial general intelligence 

      Pontes Filho, Sidney; Olsen, Kristoffer; Yazidi, Anis; Riegler, Michael; Halvorsen, Pål; Nichele, Stefano (Peer reviewed; Journal article, 2022)
      In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism ...

    • Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence 

      Kolevatova, Anastasiia; Riegler, Michael; Cherubini, Francesco; Hu, Xiangping; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2021)
      A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time ...

    • 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 ...