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Uncertainty Quantification

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TitleStatusHype
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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