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Uncertainty Quantification

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Showing 1101–1150 of 2366 papers

TitleStatusHype
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data—0
Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?Code0
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI—0
An Online Bootstrap for Time SeriesCode0
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector—0
Intrinsic Gaussian Vector Fields on ManifoldsCode0
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks—0
Bayesian Neural Controlled Differential Equations for Treatment Effect EstimationCode0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification—0
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte CarloCode0
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes ProcessCode0
Distributed Uncertainty Quantification of Kernel Interpolation on Spheres—0
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression—0
Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN samplerCode0
Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy—0
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regressionCode0
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine LearningCode0
Calibrating Neural Simulation-Based Inference with Differentiable Coverage ProbabilityCode0
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison—0
Sequential Gibbs Posteriors with Applications to Principal Component Analysis—0
PAC Prediction Sets Under Label ShiftCode0
Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse ProblemsCode0
Thin and Deep Gaussian ProcessesCode1
Online Algorithms with Uncertainty-Quantified Predictions—0
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction—0
Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification—0
Provable Probabilistic Imaging using Score-Based Generative PriorsCode1
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty—0
On the Properties and Estimation of Pointwise Mutual Information ProfilesCode1
Pseudo-Bayesian Optimization—0
Uncertainty Quantification using Generative Approach—0
Statistical guarantees for stochastic Metropolis-HastingsCode0
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors—0
Trustworthy Machine Learning—0
Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graphCode0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Deep Learning: A Tutorial—0
Hoeffding decomposition of black-box models with dependent inputs—0
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods—0
Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing modelCode0
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient KernelsCode0
Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making—0
Learning to Predict Structural VibrationsCode1
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting—0
Improved Active Learning via Dependent Leverage Score Sampling—0
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations—0
Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations—0
Sparse Deep Learning for Time Series Data: Theory and Applications—0
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural NetworksCode1
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