Results 21 to 30 of about 3,113,242 (314)
Uncertainty quantification and propagation with probability boxes
Nuclear Engineering and Technology, 2021 In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field.
L. Duran-Vinuesa, D. Cuervo
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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
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An Accurate Sample Rejection Estimator of the Outage Probability With Equal Gain Combining
IEEE Open Journal of the Communications Society, 2020 We evaluate the outage probability (OP) for L-branch equal gain combining (EGC) receivers operating over fading channels, i.e., equivalently the cumulative distribution function (CDF) of the sum of the L channel envelopes.
Nadhir Ben Rached +3 more
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Quantification of Uncertainty with Adversarial Models [PDF]
Neural Information Processing Systems, 2023 Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a ...
Kajetan Schweighofer +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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IEEE Transactions on Industrial Informatics, 2023 The automatic and accurate analysis of medical images (e.g., segmentation,detection, classification) are prerequisites for modern disease diagnosis and prognosis.
Moloud Abdar +9 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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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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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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Optimal Uncertainty Quantification [PDF]
, 2012 We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront.
Bentkus V. +18 more
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