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Uncertainty Quantification

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Showing 1651–1700 of 2366 papers

TitleStatusHype
Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints—0
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code—0
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes—0
Learning Multivariate CDFs and Copulas using Tensor Factorization—0
Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing—0
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks—0
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption—0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing—0
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis—0
Multi-fidelity Monte Carlo: a pseudo-marginal approach—0
Efficient Bayes Inference in Neural Networks through Adaptive Importance SamplingCode0
Meta-Learning Priors for Safe Bayesian Optimization—0
Causal Estimation for Text Data with (Apparent) Overlap Violations—0
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature—0
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System—0
Φ-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation—0
Label Noise Robustness of Conformal Prediction—0
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception—0
Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning modelsCode0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles—0
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography—0
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation—0
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields—0
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems—0
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network—0
Towards Improving Calibration in Object Detection Under Domain Shift—0
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace ApplicationsCode0
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series dataCode0
TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification—0
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey—0
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution—0
Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy ImagesCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Bayesian Continual Learning via Spiking Neural NetworksCode0
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives—0
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies—0
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep LearningCode0
Sub-aperture SAR Imaging with Uncertainty Quantification—0
Calibrated and Enhanced NRLMSIS 2.0 Model with Uncertainty Quantification—0
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity—0
Beta-Sorted Portfolios—0
Optimal Energy Management in Autonomous Power Systems with Probabilistic Security Constraints and Adaptive Frequency Control—0
Monte Carlo is a good sampling strategy for polynomial approximation in high dimensions—0
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics—0
Uncertainty Quantification for Traffic Forecasting: A Unified Approach—0
Multi-fidelity wavelet neural operator with application to uncertainty quantification—0
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 modelCode0
A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems—0
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