Results 41 to 50 of about 1,009,621 (180)
Applied Sciences, 2021 Aleatoric and epistemic uncertainties can be represented probabilistically in mechanical systems. However, the distribution parameters of epistemic uncertainties are also uncertain due to sparsely available or inaccurate uncertainty information ...
Yufeng Lyu +4 more
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Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference
Sensors, 2022 In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years.
Monika E. Heringhaus +3 more
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Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? [PDF]
, 2023 The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning.
Wimmer, Lisa +4 more
core +4 more sources
Uncertainty Quantification for Full-Flight Data Based Engine Fault Detection with Neural Networks
Machines, 2022 Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault ...
Matthias Weiss +4 more
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, 2021 Aleatoric Anthems and PalindromesAleatoric Anthems and Palindromes is a collection of music composed by Lars Bröndum 2020-2021. The music was performed by the Aleatoric Chamber Ensemble: My Eklund, Per Gärdin, Lisa Ullén, Lars Bröndum, Daniel Borgegård ...
Bröndum, Lars,
core
Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
IEEE Open Journal of Engineering in Medicine and BiologyUncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the ...
Enamundram Naga Karthik +2 more
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CAPO: Causal-Adaptive Preference Optimization for Diffusion Models via Causal Inference Uncertainty
IEEE AccessPost-training alignment of diffusion models based on human feedback uniformly applies equal weights to all preference pairs, ignoring the inherent aleatoric and epistemic uncertainty in feedback, leading to overfitting on noisy signals and insufficient ...
Sihan Hu
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThis article presents a method to improve the usability of lake ice cover (LIC) maps generated from moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere reflectance data by providing estimates of aleatoric and epistemic uncertainty. We
Nastaran Saberi +4 more
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Smart Agricultural Technology, 2023 The combination of computer vision with deep learning has become a popular tool for automation of labor-intensive monitoring tasks in modern livestock farming.
Christian Lamping +2 more
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Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source