Results 261 to 270 of about 110,431 (303)
Dynamic Mode Decomposition accelerates uncertainty quantification via Polynomial Chaos Expansion
D. M. Tartakovsky +2 more
core
Uncertainty Quantification for Text Classification
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 This full-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and epistemic uncertainty for text classification models.
Dell Zhang +5 more
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges [PDF]
Information Fusion, 2021 Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes.
Moloud Abdar +2 more
exaly +2 more sources
, 2021 What are the limitations of scientific models? Uncertainty quantification, Sensitivity analyses, Theory-based and statistical modeling, Systems thinking, systems ...
Behnam Sadeghi +2 more
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Oberwolfach Reports, 2020
Uncertainty quantification (UQ) is concerned with including and characterising uncertainties in mathematical models. Major steps comprise proper description of system uncertainties, analysis and efficient quantification of uncertainties in predictions and design problems, and statistical inference on uncertain parameters starting from available ...
Ernst, Oliver +3 more
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Uncertainty quantification (UQ) is concerned with including and characterising uncertainties in mathematical models. Major steps comprise proper description of system uncertainties, analysis and efficient quantification of uncertainties in predictions and design problems, and statistical inference on uncertain parameters starting from available ...
Ernst, Oliver +3 more
openaire +3 more sources
Uncertainty Quantification in Optimization
2019We consider constrained optimization problems affected by uncertainty, where the objective function or the restrictions involve random variables \( \varvec{u} \). In this situation, the solution of the optimization problem is a random variable \( \varvec{x}\left( \varvec{u} \right) \): we are interested in the determination of its distribution of ...
Eduardo Souza de Cursi +1 more
openaire +1 more source
2017
This book results from a course developed by the author and reflects both his own and collaborative research regarding the development and implementation of uncertainty quantification (UQ) techniques for large-scale applications over the last two decades.The objectives of this book are to present fundamental notions for the stochastic modeling of ...
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This book results from a course developed by the author and reflects both his own and collaborative research regarding the development and implementation of uncertainty quantification (UQ) techniques for large-scale applications over the last two decades.The objectives of this book are to present fundamental notions for the stochastic modeling of ...
+5 more sources
Special Issue on Uncertainty Quantification
SIAM Journal on Scientific Computing, 2004Following the substantial interest in the uncertainty quantification (UQ) sessions at the Second SIAM Computational Science and Engineering Conference in San Diego on February 10--13, 2003, a proposal to organize a special issue of SISC was approved by the SIAM advisory board.
Roger G. Ghanem, Steven F. Wojtkiewicz
openaire +1 more source
2019
Uncertainty quantification (UQ) is concerned with including and characterising uncertainties in mathematical models. Major steps comprise proper description of system uncertainties, analysis and efficient quantification of uncertainties in predictions and design problems, and statistical inference on uncertain parameters starting from available ...
openaire +2 more sources
Uncertainty quantification (UQ) is concerned with including and characterising uncertainties in mathematical models. Major steps comprise proper description of system uncertainties, analysis and efficient quantification of uncertainties in predictions and design problems, and statistical inference on uncertain parameters starting from available ...
openaire +2 more sources
Uncertainty Quantifications for Multiviewcorrelation
The aim of this extended abstract is to introduce a general framework for the quantifications of uncertainties associated with displacement measurements via multiview correlation. The latter is an extension of stereocorrelation approaches to systems with more than two cameras.Hild, François, Roux, Stephane
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