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Uncertainty Quantification

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Showing 1101–1150 of 2366 papers

TitleStatusHype
Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning—0
Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification—0
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity—0
Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals—0
Enhancing In-Hospital Mortality Prediction Using Multi-Representational Learning with LLM-Generated Expert Summaries—0
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba—0
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning—0
Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification—0
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems—0
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks—0
Bridging the Sim-to-Real Gap with Bayesian Inference—0
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies—0
Enhanced multi-fidelity modelling for digital twin and uncertainty quantification—0
End-to-End Probabilistic Framework for Learning with Hard Constraints—0
Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability—0
End-to-end Conditional Robust Optimization—0
Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation—0
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling—0
Emulator-based global sensitivity analysis for flow-like landslide run-out models—0
Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review—0
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification—0
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data—0
Adversarial Attacks Against Uncertainty Quantification—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions—0
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
Black-box Uncertainty Quantification Method for LLM-as-a-Judge—0
Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Approximately Piecewise E(3) Equivariant Point Networks—0
Advances in Surrogate Modeling for Biological Agent-Based Simulations: Trends, Challenges, and Future Prospects—0
Uncertainty-aware multi-fidelity surrogate modeling with noisy data—0
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network—0
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning—0
Beyond the Norms: Detecting Prediction Errors in Regression Models—0
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA—0
Efficient Line Search Method Based on Regression and Uncertainty Quantification—0
Uncertainty Quantification and Exploration for Reinforcement Learning—0
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