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Uncertainty Quantification

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Showing 2051–2100 of 2366 papers

TitleStatusHype
DUST: A Framework for Data-Driven Density Steering—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Hidden Markov Neural Networks—0
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription—0
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation—0
EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations—0
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors—0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
Efficient Conformal Prediction under Data Heterogeneity—0
Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking—0
Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation—0
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Uncertainty Quantification and Exploration for Reinforcement Learning—0
Efficient Line Search Method Based on Regression and Uncertainty Quantification—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling—0
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network—0
Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics—0
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification—0
Emulator-based global sensitivity analysis for flow-like landslide run-out models—0
End-to-end Conditional Robust Optimization—0
End-to-End Probabilistic Framework for Learning with Hard Constraints—0
Enhanced multi-fidelity modelling for digital twin and uncertainty quantification—0
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks—0
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems—0
Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification—0
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba—0
Enhancing In-Hospital Mortality Prediction Using Multi-Representational Learning with LLM-Generated Expert Summaries—0
Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals—0
Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification—0
Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference—0
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy—0
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows—0
Ensemble Neural Networks (ENN): A gradient-free stochastic method—0
Entity Aware Modelling: A Survey—0
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction—0
Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Epinet for Content Cold Start—0
Epistemic Deep Learning—0
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