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Uncertainty Quantification

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Showing 376–400 of 2366 papers

TitleStatusHype
Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks—0
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods—0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images—0
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises—0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty—0
A computational spectral approach to interest rate models—0
Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning—0
Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization—0
Are generative models fair? A study of racial bias in dermatological image generation—0
A recursive Bayesian neural network for constitutive modeling of sands under monotonic loading—0
A framework for benchmarking uncertainty in deep regression—0
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions—0
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—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
Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks—0
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector—0
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
Composing Normalizing Flows for Inverse Problems—0
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks—0
Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability—0
Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation—0
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