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Uncertainty Quantification

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Showing 1601–1650 of 2366 papers

TitleStatusHype
CUQDS: Conformal Uncertainty Quantification under Distribution Shift for Trajectory Prediction—0
Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks—0
Adaptive Uncertainty Quantification for Generative AI—0
A data-centric weak supervised learning for highway traffic incident detection—0
A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian Province—0
A data-driven model order reduction approach for Stokes flow through random porous media—0
A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch—0
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI—0
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification—0
A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
A Deep Learning Approach to Dst Index Prediction—0
A Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI—0
A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures—0
A design specification for Critical Illness Digital Twins to cure sepsis: responding to the National Academies of Sciences, Engineering and Medicine Report: Foundational Research Gaps and Future Directions for Digital Twins—0
A Dictionary Approach to EBSD Indexing—0
A Distributionally Robust Approach to Fair Classification—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Advances in Surrogate Modeling for Biological Agent-Based Simulations: Trends, Challenges, and Future Prospects—0
Adversarial Attacks Against Uncertainty Quantification—0
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions—0
A framework for benchmarking uncertainty in deep regression—0
A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty—0
A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images—0
A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers—0
A General Framework for Uncertainty Quantification via Neural SDE-RNN—0
A generalized Bayes framework for probabilistic clustering—0
A generative foundation model for an all-in-one seismic processing framework—0
Aggregation of Models, Choices, Beliefs, and Preferences—0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification—0
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling—0
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification—0
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—0
AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage—0
AI-Powered Bayesian Inference—0
AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts—0
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees—0
A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems—0
Algorithms with Calibrated Machine Learning Predictions—0
Aligned Multi-Task Gaussian Process—0
All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty—0
A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty—0
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations—0
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation—0
A machine learning approach for efficient uncertainty quantification using multiscale methods—0
A Model-Constrained Discontinuous Galerkin Network (DGNet) for Compressible Euler Equations with Out-of-Distribution Generalization—0
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