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Uncertainty Quantification

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Showing 851–900 of 2366 papers

TitleStatusHype
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks—0
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model—0
AI-Powered Bayesian Inference—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
Composing Normalizing Flows for Inverse Problems—0
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors—0
Conditional Neural Processes for Molecules—0
Asymmetric Duos: Sidekicks Improve Uncertainty—0
AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage—0
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling—0
A Survey on Uncertainty Toolkits for Deep Learning—0
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions—0
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality—0
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations—0
Conditional Distribution Quantization in Machine Learning—0
Conditional Diffusion-Based Retrieval of Atmospheric CO2 from Earth Observing Spectroscopy—0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
Conditional Coverage Estimation for High-quality Prediction Intervals—0
Conditional Conformal Risk Adaptation—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification—0
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks—0
Computing High-dimensional Confidence Sets for Arbitrary Distributions—0
Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century—0
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Complementing Semi-Supervised Learning with Uncertainty Quantification—0
A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning—0
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty—0
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification—0
Comparing the quality of neural network uncertainty estimates for classification problems—0
A Statistical Machine Learning Approach for Adapting Reduced-Order Models using Projected Gaussian Process—0
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
Active Inference or Control as Inference? A Unifying View—0
Accelerating Markov Random Field Inference with Uncertainty Quantification—0
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification—0
Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation—0
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Assumption-free fidelity bounds for hardware noise characterization—0
Combined State and Parameter Estimation in Level-Set Methods—0
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression—0
Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate—0
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification—0
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