Results 91 to 100 of about 1,009,621 (180)

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation. [PDF]

J Med Imaging (Bellingham), 2022
Jones CK, Wang G, Yedavalli V, Sair H.
europepmc   +1 more source

Embracing uncertainty in time: A Bayesian dynamic mixing model for isotopic diet estimation

Methods in Ecology and Evolution, Volume 17, Issue 10, Page 2928-2942, October 2026.
Abstract Isotopic mixing models are powerful tools in ecology for estimating diets. Since their initial formulation, these models have evolved into two distinct branches: Bayesian mixing models and dynamic mixing models. Bayesian mixing models significantly improve uncertainty management but remain static assuming a stable state.
Emilie Cathelin, Sébastien Lefebvre, Carolina Giraldo, Andrew Parnell   +3 more
wiley   +1 more source

Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks

Sensors
Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely ...
Alireza Nezhadettehad, Arkady Zaslavsky, Abdur Rakib, Seng W. Loke   +3 more
doaj   +1 more source

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Journal of Geophysical Research: Machine Learning and Computation
Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high‐dimensional, correlated data.
Harrison J. Goldwyn, Mitchell Krock, Johann Rudi, Daniel Getter, Julie Bessac   +4 more
doaj   +1 more source

Cross-Modal Interaction Fusion-Based Uncertainty-Aware Prediction Method for Industrial Froth Flotation Concentrate Grade by Using a Hybrid SKNet-ViT Framework

Sensors
In froth flotation, the features of froth images are important information to predict the concentrate grade. However, the froth structure is influenced by multiple factors, such as air flowrate, slurry level, ore properties, reagents, etc., which leads ...
Fanlei Lu, Weihua Gui, Yulong Wang, Jiayi Zhou, Xiaoli Wang   +4 more
doaj   +1 more source

Am I confused or is this confusing?: Deep ensembles for ENSO uncertainty quantification

Machine Learning. Earth
Faithful uncertainty quantification (UQ) is paramount in high stakes climate prediction. Deep ensembles, or ensembles of probabilistic neural networks, are state of the art for UQ in machine learning (ML) and are growing increasingly popular for weather ...
Devin M McAfee, Elizabeth A Barnes
doaj   +1 more source

Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations

Entropy
Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification.
Timothy Davey
doaj   +1 more source

Piecewise Linear Transformation – Propagating Aleatoric Uncertainty in Neural Networks [PDF]

Real-world data typically exhibit aleatoric uncertainty which has to be considered during data-driven decision-making to assess the confidence of the decision provided by machine learning models.
Krapf, Thomas, Heinrich, Bernd, Miethaner, Paul, Hagn, Michael, Luttner, Lucas, Schiller, Alexander   +5 more
core   +2 more sources

Uncertainty-aware deep learning for wildfire danger forecasting

Machine Learning. Earth
Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forecasting models that are not only accurate but also reliable. deep learning (
Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls, Ioannis Papoutsis   +3 more
doaj   +1 more source

Hybrid data augmentation for uncertainty mitigation in machine learning based fault diagnosis of pressurized water reactor nuclear power plants

Nuclear Engineering and Technology
Uncertainty in machine learning-based fault diagnosis for nuclear power plants (NPPs) poses a critical challenge due to limited and noisy operational data.
Fazle Haseeb, Minjun Peng, Furqan Arshad, Khan Awais, Wasiq Ali   +4 more
doaj   +1 more source