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Uncertainty Quantification

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Showing 2151–2200 of 2366 papers

TitleStatusHype
Causal Bayesian Optimization—0
Multi-view polarimetric scattering cloud tomography and retrieval of droplet size—0
A Bayesian - Deep Learning model for estimating Covid-19 evolution in SpainCode0
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors—0
A Closed-Form Uncertainty Propagation in Non-Rigid Structure from MotionCode0
Large-scale Uncertainty Estimation and Its Application in Revenue Forecast of SMEs—0
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models—0
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network—0
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix ApproximationCode0
Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics—0
Hidden Markov Neural Networks—0
Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring—0
Stochastic spectral embedding—0
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning—0
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model CalibrationCode0
Interval Neural Networks as Instability Detectors for Image ReconstructionsCode0
Interval Neural Networks: Uncertainty ScoresCode0
On Calibration of Mixup Training for Deep Neural NetworksCode0
Nearest Neighbor Dirichlet MixturesCode0
Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing—0
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data—0
Scalable Uncertainty for Computer Vision with Functional Variational Inference—0
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery—0
Composing Normalizing Flows for Inverse Problems—0
Uncertainty Quantification for Sparse Deep Learning—0
A Comparative Study of Machine Learning Models for Predicting the State of Reactive MixingCode0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
Statistical aspects of nuclear mass models—0
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation—0
How Good is the Bayes Posterior in Deep Neural Networks Really?—0
Uncertainty Quantification for Bayesian Optimization—0
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models—0
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions—0
Certified and fast computations with shallow covariance kernels—0
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty EstimationCode0
Finding Optimal Points for Expensive Functions Using Adaptive RBF-Based Surrogate Model Via Uncertainty Quantification—0
Building high accuracy emulators for scientific simulations with deep neural architecture search—0
Tackling small eigen-gaps: Fine-grained eigenvector estimation and inference under heteroscedastic noise—0
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantificationCode0
Considering discrepancy when calibrating a mechanistic electrophysiology modelCode0
Healing Gaussian Process Experts—0
On Semi-parametric Inference for BART—0
Machine Learning for Clouds and Climate—0
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network—0
Optimal Uncertainty-guided Neural Network Training—0
A practical guide to pseudo-marginal methods for computational inference in systems biologyCode0
Detection of False Positive and False Negative Samples in Semantic SegmentationCode0
Solving Bayesian Inverse Problems via Variational Autoencoders—0
Regression with Uncertainty Quantification in Large Scale Complex Data—0
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