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Uncertainty Quantification

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TitleStatusHype
Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement—0
Sequential Gibbs Posteriors with Applications to Principal Component Analysis—0
Sequential image recovery using joint hierarchical Bayesian learning—0
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception—0
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting—0
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification—0
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network—0
SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals—0
SleepMore: Inferring Sleep Duration at Scale via Multi-Device WiFi Sensing—0
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification—0
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions—0
Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics—0
SOFARI-R: High-Dimensional Manifold-Based Inference for Latent Responses—0
SoftBart: Soft Bayesian Additive Regression Trees—0
Solving Bayesian Inverse Problems via Variational Autoencoders—0
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data—0
Source-to-Source Automatic Differentiation of OpenMP Parallel Loops—0
Sparse Bayesian Lasso via a Variable-Coefficient _1 Penalty—0
Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis—0
Sparse Deep Learning for Time Series Data: Theory and Applications—0
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Data-driven modeling and prediction of microglial cell dynamics in the ischemic penumbra—0
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications—0
Sparse representation for damage identification of structural systems—0
Sparse Uncertainty Representation in Deep Learning with Inducing Weights—0
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