Results 51 to 60 of about 78,109 (159)
Uncertainty quantification under group sparsity [PDF]
Biometrika, 2017 Quantifying the uncertainty in penalized regression under group sparsity is an important open question. We establish, under a high-dimensional scaling, the asymptotic validity of a modified parametric bootstrap method for the group lasso, assuming a Gaussian error model and mild conditions on the design matrix and the true coefficients.
Zhou, Qing, Min, Seunghyun
openaire +2 more sources
Uncertainty quantification by large language models
Machine Learning with ApplicationsAs reasoning capabilities of large language models (LLMs) continue to advance, they are being integrated into increasingly complex scientific workflows, with the goal of developing agents capable of generating evidence-based explanations and testing ...
Dorianis M. Perez +2 more
doaj +1 more source
Artificial Intelligence in the Life SciencesThe estimation of uncertainties associated with predictions from quantitative structure–activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources ...
Hannah Rosa Friesacher +5 more
doaj +1 more source
Present and Future of Model Uncertainty Quantification in Process Systems Engineering
Chemical Engineering Transactions, 2019 This contribution investigates the impact of model uncertainty quantification techniques in different areas of process systems engineering (PSE), namely dynamic optimization, predictive maintenance, soft-sensor systems and risk assessment, using three ...
Francesco Rossi +3 more
doaj +1 more source
Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications
IEEE Access, 2018 Uncertainty quantification plays a critical role in the process of decision making and optimization in many fields of science and engineering. The field has gained an overwhelming attention among researchers in recent years resulting in an arsenal of ...
H. M. Dipu Kabir +3 more
doaj +1 more source
Uncertainty quantification and optimal decisions [PDF]
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017 A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and ...
openaire +4 more sources
Uncertainty quantification for deep learning
Environmental Data ScienceWe present a critical survey on the consistency of uncertainty quantification used in deep learning and highlight partial uncertainty coverage and many inconsistencies.
Peter Jan van Leeuwen +2 more
doaj +1 more source
Optimal Uncertainty Quantification
, 2011 We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call Optimal Uncertainty Quantification (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal ...
Owhadi, H. +4 more
openaire +2 more sources
An Uncertainty-Aware Visual System for Image Pre-Processing
Journal of Imaging, 2018 Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety.
Christina Gillmann +4 more
doaj +1 more source
International Journal of Aerospace Engineering, 2017 Airfoil geometric uncertainty can generate aerodynamic characteristics fluctuations. Uncertainty quantification is applied to compute its impact on the aerodynamic characteristics. In addition, the contribution of each uncertainty variable to aerodynamic
Xiaojing Wu, Weiwei Zhang, Shufang Song
doaj +1 more source