Results 11 to 20 of about 3,113,242 (314)
Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis [PDF]
Atmospheric Chemistry and Physics, 2020 An estimated 0.5–1 billion people globally have inadequate intakes of selenium (Se), due to a lack of bioavailable Se in agricultural soils. Deposition from the atmosphere, especially through precipitation, is an important source of Se to soils. However,
A. Feinberg +8 more
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A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods [PDF]
arXiv.org, 2023 The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption
Ling Huang +3 more
semanticscholar +1 more source
Federated Conformal Predictors for Distributed Uncertainty Quantification [PDF]
International Conference on Machine Learning, 2023 Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal prediction
Charles Lu +4 more
semanticscholar +1 more source
The Gaussian Process Modeling Module in UQLab [PDF]
Journal of Soft Computing in Civil Engineering, 2018 We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a ...
Christos Lataniotis +2 more
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Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image ...
Omer Belhasin +4 more
semanticscholar +1 more source
Geometry of martensite needles in shape memory alloys
Comptes Rendus. Mathématique, 2021 We study the geometry of needle-shaped domains in shape-memory alloys. Needle-shaped domains are ubiquitously found in martensites around macroscopic interfaces between regions which are laminated in different directions, or close to macroscopic ...
Conti, Sergio +4 more
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A Benchmark on Uncertainty Quantification for Deep Learning Prognostics [PDF]
Reliability Engineering & System Safety, 2023 Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for prognostics deep ...
Luis Basora +3 more
semanticscholar +1 more source
Quantum-Inspired Uncertainty Quantification
Frontiers in Computer Science, 2022 Reasonable quantification of uncertainty is a major issue of cognitive infocommunications, and logic is a backbone for successful communication. Here, an axiomatic approach to quantum logic, which highlights similarity to and differences to classical ...
Günther Wirsching
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Uncertainty in Natural Language Processing: Sources, Quantification, and Applications [PDF]
arXiv.org, 2023 As a main field of artificial intelligence, natural language processing (NLP) has achieved remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in a unified manner, with various tasks being associated with each other ...
Mengting Hu +4 more
semanticscholar +1 more source
Contaminant source localization via Bayesian global optimization [PDF]
Hydrology and Earth System Sciences, 2019 Contaminant source localization problems require efficient and robust methods that can account for geological heterogeneities and accommodate relatively small data sets of noisy observations.
G. Pirot +7 more
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