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Uncertainty Quantification

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Showing 226–250 of 2366 papers

TitleStatusHype
Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model—0
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content—0
NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis PredictionCode0
Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey—0
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine LearningCode3
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes—0
Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence—0
Bayesian Modeling of Zero-Shot Classifications for Urban Flood DetectionCode0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Quantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inference—0
These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models—0
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian SensorsCode0
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal predictionCode0
On Local Posterior Structure in Deep EnsemblesCode0
E-Values Expand the Scope of Conformal PredictionCode0
H-AddiVortes: Heteroscedastic (Bayesian) Additive Voronoi Tessellations—0
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful LifeCode1
Probabilistic Neural Networks (PNNs) with t-Distributed Outputs: Adaptive Prediction Intervals Beyond Gaussian Assumptions—0
Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural NetworksCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy PredictionCode1
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