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Uncertainty Quantification

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Showing 901–950 of 2366 papers

TitleStatusHype
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
Federated Data Analytics: A Study on Linear Models—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Assumption-free fidelity bounds for hardware noise characterization—0
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models—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
Fast Uncertainty Quantification for Deep Object Pose Estimation—0
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials—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
Assessment of Uncertainty Quantification in Universal Differential Equations—0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks—0
Fast Value Tracking for Deep Reinforcement Learning—0
Fast Likelihood-Free Parameter Estimation for Lévy Processes—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Collaborative Deterministic-Probabilistic Forecasting for Real-World Spatiotemporal Systems—0
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework—0
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction—0
FedPop: A Bayesian Approach for Personalised Federated Learning—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Feedforward neural networks as statistical models: Improving interpretability through uncertainty quantification—0
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves—0
(f,Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems—0
Aggregation of Models, Choices, Beliefs, and Preferences—0
Fair Uncertainty Quantification for Depression Prediction—0
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks—0
Fairness Uncertainty Quantification: How certain are you that the model is fair?—0
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion—0
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification—0
CoCoAFusE: Beyond Mixtures of Experts via Model Fusion—0
Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency—0
α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction—0
Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics—0
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation—0
Forecasting the future development in quality and value of professional football players for applications in team management—0
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes—0
Accelerating Ensemble Error Bar Prediction with Single Models Fits—0
Flow Matching Meets PDEs: A Unified Framework for Physics-Constrained Generation—0
Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere—0
Frequentist Guarantees of Distributed (Non)-Bayesian Inference—0
Extracting Dynamical Models from Data—0
Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification—0
Extending the Scope of Inference About Predictive Ability to Machine Learning Methods—0
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach—0
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