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Uncertainty Quantification

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Showing 1926–1950 of 2366 papers

TitleStatusHype
CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition—0
Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks—0
Constraining the Dynamics of Deep Probabilistic Models—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Continual learning via probabilistic exchangeable sequence modelling—0
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation—0
Continuum Limit of Posteriors in Graph Bayesian Inverse Problems—0
Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation—0
Controlled Dropout for Uncertainty Estimation—0
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery—0
Correcting for Interference in Experiments: A Case Study at Douyin—0
Measurement Error and Counterfactuals in Quantitative Trade and Spatial Models—0
Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials—0
Coupling the reduced-order model and the generative model for an importance sampling estimator—0
Covariance-Free Sparse Bayesian Learning—0
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks—0
Crowd Counting with Decomposed Uncertainty—0
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty—0
Current Methods for Drug Property Prediction in the Real World—0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Cycles of Thought: Measuring LLM Confidence through Stable Explanations—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
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