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Uncertainty Quantification

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Showing 1501–1550 of 2366 papers

TitleStatusHype
EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations—0
Fairness Uncertainty Quantification: How certain are you that the model is fair?—0
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions—0
Score-based Generative Modeling Through Backward Stochastic Differential Equations: Inversion and Generation—0
A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill RecognitionCode0
Quantile Extreme Gradient Boosting for Uncertainty Quantification—0
Multidimensional Uncertainty Quantification for Deep Neural Networks—0
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks—0
Uncertainty-Aware Null Space Networks for Data-Consistent Image ReconstructionCode0
Neural State-Space Models: Empirical Evaluation of Uncertainty QuantificationCode0
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision—0
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring—0
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model—0
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning—0
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantificationCode0
Perspectives on AI Architectures and Co-design for Earth System Predictability—0
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry—0
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine LearningCode0
Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control—0
Multilevel CNNs for Parametric PDEs—0
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Laplacian Segmentation Networks Improve Epistemic Uncertainty QuantificationCode0
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data—0
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical AnalysisCode0
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review—0
Adaptive Modeling of Uncertainties for Traffic Forecasting—0
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field—0
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review—0
Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity conceptsCode0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
Lifelong Machine Learning Potentials—0
Uncertainty quantification in neural network classifiers -- a local linear approach—0
HappyMap: A Generalized Multi-calibration Method—0
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural NetworksCode0
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification—0
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis—0
Expectation consistency for calibration of neural networksCode0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing SymmetriesCode0
Multi-Head Multi-Loss Model CalibrationCode0
Toward Robust Uncertainty Estimation with Random Activation FunctionsCode0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty AlignmentCode0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
Calibrated Regression Against An Adversary Without Regret—0
Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting—0
Improved uncertainty quantification for neural networks with Bayesian last layerCode0
Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshotsCode0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
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