Results 31 to 40 of about 3,213,103 (353)
Multidimensional integration using machine learning and Monte Carlo methods for acoustic predictions [PDF]
ESAIM: Proceedings and SurveysTo predict underwater noise radiated by a ship, various numerical methods are available. In underwater acoustics, the most effective prediction methods consist in solving an acoustic analogy using an integral formulation.
Coiffard Théo +5 more
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Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis [PDF]
Artif. Intell. Medicine, 2022 The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL models.
Benjamin Lambert +5 more
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GENERATIONS IN BAYESIAN NETWORKS
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019 This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko +3 more
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Uncertainty Quantification of Imperfect Diagnostics
Aerospace, 2023 The operable state of a system is maintained during operation, which requires knowledge of the system’s state. Technical diagnostics, as a process of accurately obtaining information about the system state, becomes a crucial stage in the life cycle of ...
Vladimir Ulansky, Ahmed Raza
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Geoscience Data Journal, 2022 The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty.
Hendrik Paasche +4 more
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Evidential Uncertainty Quantification: A Variance-Based Perspective [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023 Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in evidential deep learning shed light on the direct quantification of
Ruxiao Duan +4 more
semanticscholar +1 more source
Towards Best Practice Framing of Uncertainty in Scientific Publications: A Review of Water Resources Research Abstracts [PDF]
, 2017 Uncertainty is recognized as a key issue in water resources research, amongst other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g. uncertainty quantification and model validation.
Elsawah, Sondoss +4 more
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Uncertainty Quantification in Sunspot Counts [PDF]
The Astrophysical Journal, 2019 Abstract Observing and counting sunspots constitutes one of the longest-running scientific experiments, with first observations dating back to Galileo (around 1610). Today the sunspot number (SN) time series acts as a benchmark of solar activity in a large range of physical models. An appropriate statistical modeling, adapted to the time
Sophie Mathieu +4 more
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Algorithms, 2022 Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase.
Sihmehmet Yildiz +2 more
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Fortuna: A Library for Uncertainty Quantification in Deep Learning [PDF]
Journal of machine learning research, 2023 We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that can be applied to any trained neural network to generate reliable ...
Gianluca Detommaso +5 more
semanticscholar +1 more source