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Uncertainty Quantification

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Showing 776–800 of 2366 papers

TitleStatusHype
Robust Classification by Coupling Data Mollification with Label SmoothingCode0
Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward ModelCode3
Bayesian Joint Additive Factor Models for Multiview LearningCode0
A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning—0
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning ApproachCode0
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantificationCode1
Uncertainty Quantification for Deep Learning—0
Uncertainty Quantification for Bird's Eye View Semantic Segmentation: Methods and Benchmarks—0
Bayesian Uncertainty Quantification for Anaerobic Digestion models—0
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture—0
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic SimilaritiesCode1
Conformal Depression PredictionCode0
Valid Conformal Prediction for Dynamic GNNsCode0
Verifiably Robust Conformal PredictionCode0
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal PredictionCode0
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantificationCode0
SEMF: Supervised Expectation-Maximization Framework for Predicting IntervalsCode0
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba—0
Kernel-based Optimally Weighted Conformal Prediction Intervals—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
Conformal Prediction for Ensembles: Improving Efficiency via Score-Based AggregationCode0
Uncertainty Quantification for Neurosymbolic Programs via Compositional Conformal Prediction—0
Hierarchical Uncertainty Exploration via Feedforward Posterior Trees—0
ProDAG: Projected Variational Inference for Directed Acyclic GraphsCode0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
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