Results 81 to 90 of about 10,063 (188)

One Step Closer to Unbiased Aleatoric Uncertainty Estimation

Proceedings of the AAAI Conference on Artificial Intelligence
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty.
Zhang, Wang, Ma, Ziwen, Das, Subhro, Weng, Tsui-Wei, Megretski, Alexandre, Daniel, Luca, Nguyen, Lam M.   +6 more
openaire   +2 more sources

Field-Level Uncertainty Quantification for AI-Based Ship Hull Surface Pressure Prediction

Journal of Marine Science and Engineering
This study investigates uncertainty quantification for field-level ship hull surface pressure predictions using a U-Net-based data-driven model. A speed-conditioned U-Net is trained on a large CFD dataset covering multiple ship types and velocity ...
Jeongbeom Seo, Inwon Lee
doaj   +1 more source

Bonnefoy traducteur : à quoi bon encore des sonnets ?

Etudes Epistémè, 2004
Since 1994, Yves Bonnefoy has been publishing his translation of Shakespeare’s Sonnets in installments. He has chosen an irregular form: each poem boasts between 14 and 20 unrhymed lines, although he claims to be always following « a (strict) four-stanza
Bertrand Degott
doaj   +1 more source

An uncertainty-aware domain adaptive semantic segmentation framework

Autonomous Intelligent Systems
Semantic segmentation is significant to realize the scene understanding of autonomous driving. Due to the lack of annotated real-world data, the technology of domain adaptation is applied so that the model is trained on the synthetic data and inferred on
Huilin Yin, Pengyu Wang, Boyu Liu, Jun Yan   +3 more
doaj   +1 more source

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning

IEEE Access
This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity.
Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton   +2 more
doaj   +1 more source

STACI: Spatio-Temporal Aleatoric Conformal Inference

Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while scalable, typically assume a simplistic independent covariance matrix for the response, failing to capture the underlying correlation structure.
Feng, Brandon R., Park, David Keetae, Luo, Xihaier, Urdangarin, Arantxa, Yoo, Shinjae, Reich, Brian J.   +5 more
openaire   +2 more sources

Informing synthetic passive microwave predictions through Bayesian deep learning with uncertainty decomposition

Environmental Data Science
Space-borne passive microwave (PMW) data provide rich information on atmospheric state, including cloud structure and underlying surface properties. However, PMW data are sparse and limited due to low Earth orbit collection, resulting in coarse Earth ...
Pedro Ortiz, Eleanor Casas, Marko Orescanin, Scott W. Powell, Veljko Petkovic   +4 more
doaj   +1 more source

Toward a New Flood Assessment Paradigm: From Exceedance Probabilities to the Expected Maximum Floods and Damages

Water Resources Research
To assess flood risks, we seek to estimate the probability distribution of the worst possible single‐event over a contiguous period of N years rather than the cumulative losses expected over a planning horizon.
E. Todini, P. Reggiani
doaj   +1 more source

A Trustworthy Model With Uncertainty Management for Predicting Vascular Access Dysfunction in Hemodialysis Patients

IEEE Access
Vascular access dysfunction is a prevalent and critical complication among hemodialysis patients, particularly in Taiwan, which has the highest proportion of dialysis patients globally. Early diagnosis and effective management are essential for improving
Chia-Hsun Lin, Zheng Lin Chen, Chung-Kuan Wu, Tsen-Che Wu, Ching-Wen Ma   +4 more
doaj   +1 more source

Label-wise Aleatoric and Epistemic Uncertainty Quantification

Uncertainty in Artificial Intelligence.
Sale, Yusuf, Hofman, Paul, Löhr, Timo, Wimmer, Lisa, Nagler, Thomas, Hüllermeier, Eyke   +5 more
openaire   +4 more sources