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Uncertainty Quantification

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TitleStatusHype
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves—0
Uncertainty Quantification in Inverse Models in Hydrology—0
Subsurface Characterization using Ensemble-based Approaches with Deep Generative ModelsCode0
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal RetrievalCode1
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior—0
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective—0
LoRA ensembles for large language model fine-tuning—0
Uncertainty Quantification for Eosinophil Segmentation—0
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy MaximizationCode0
Uncertainty Quantification via Neural Posterior Principal Components—0
The Robust Semantic Segmentation UNCV2023 Challenge Results—0
Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design—0
Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach—0
DPA-WNO: A gray box model for a class of stochastic mechanics problem—0
On the Posterior Distribution in Denoising: Application to Uncertainty QuantificationCode1
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science ApplicationsCode1
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous SpaceCode1
Explaining Predictive Uncertainty with Information Theoretic Shapley ValuesCode1
Uncertainty-driven Exploration Strategies for Online Grasp Learning—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsCode0
Multi-fidelity climate model parameterization for better generalization and extrapolation—0
Using AI Uncertainty Quantification to Improve Human Decision-Making—0
Adversarial Attacks Against Uncertainty Quantification—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge—0
Uncertainty Quantification of Autoencoder-based Koopman Operator—0
A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo—0
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantificationCode0
Scalable Model-Based Gaussian Process Clustering—0
Uncertainty quantification for learned ISTA—0
Tree of Uncertain Thoughts Reasoning for Large Language Models—0
Physics-informed Bayesian inference of external potentials in classical density-functional theory—0
When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System—0
Uncertainty-aware Traffic Prediction under Missing DataCode0
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models—0
Promises of Deep Kernel Learning for Control Synthesis—0
Medical Image Segmentation with Belief Function Theory and Deep Learning—0
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivityCode0
Band-gap regression with architecture-optimized message-passing neural networksCode0
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentationCode1
Physics-informed reinforcement learning via probabilistic co-adjustment functions—0
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident PredictionCode1
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics—0
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty—0
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Bayes' Rays: Uncertainty Quantification for Neural Radiance FieldsCode1
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman FilterCode1
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