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Uncertainty Quantification

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Showing 901–950 of 2366 papers

TitleStatusHype
Posterior Uncertainty Quantification in Neural Networks using Data AugmentationCode0
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data—0
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects—0
Function-space Parameterization of Neural Networks for Sequential LearningCode0
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning—0
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEsCode0
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification—0
Uncertainty Quantification for cross-subject Motor Imagery classificationCode0
A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty—0
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network PosteriorsCode0
Towards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection—0
Discovering High-Strength Alloys via Physics-Transfer Learning—0
Scalable Spatiotemporal Prediction with Bayesian Neural FieldsCode2
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation—0
Koopman Ensembles for Probabilistic Time Series ForecastingCode0
Model-Free Local Recalibration of Neural Networks—0
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification—0
End-to-end Conditional Robust Optimization—0
Confidence on the Focal: Conformal Prediction with Selection-Conditional CoverageCode0
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing—0
Uncertainty quantification for deeponets with ensemble kalman inversion—0
Conformal prediction for multi-dimensional time series by ellipsoidal setsCode1
Scalable Bayesian inference for the generalized linear mixed model—0
Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciencesCode0
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming—0
On Fractional Moment Estimation from Polynomial Chaos Expansion—0
Statistical Mechanics of Dynamical System Identification—0
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models—0
Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD DetectionCode0
Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysisCode0
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized TasksCode2
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural NetworksCode0
Outlier-Detection for Reactive Machine Learned Potential Energy SurfacesCode0
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification—0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations—0
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction—0
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification—0
Quantifying neural network uncertainty under volatility clustering—0
Uncertainty-Aware Evaluation for Vision-Language ModelsCode1
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Accuracy-Preserving Calibration via Statistical Modeling on Probability SimplexCode0
Extending the Scope of Inference About Predictive Ability to Machine Learning Methods—0
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods—0
Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks—0
Uncertainty quantification in fine-tuned LLMs using LoRA ensemblesCode0
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes—0
Training Bayesian Neural Networks with Sparse Subspace Variational InferenceCode0
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