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Uncertainty Quantification

Papers

Showing 251300 of 2366 papers

TitleStatusHype
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty QuantificationCode1
AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation ModelsCode1
Masksembles for Uncertainty EstimationCode1
Probabilistic Contrastive Principal Component AnalysisCode1
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationCode1
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium MoleculesCode1
Uncertainty estimation for molecular dynamics and samplingCode1
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational ImagingCode1
Incorporating Interpretable Output Constraints in Bayesian Neural NetworksCode1
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric SplittingCode1
Learnable Uncertainty under Laplace ApproximationsCode1
Neural BootstrapperCode1
Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python libraryCode1
Uncertainty Sets for Image Classifiers using Conformal PredictionCode1
SDE-Net: Equipping Deep Neural Networks with Uncertainty EstimatesCode1
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image PriorCode1
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image SegmentationCode1
Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty QuantificationCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Uncertainty Quantification and Deep EnsemblesCode1
Extended Stochastic Block Models with Application to Criminal NetworksCode1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty QuantificationCode1
Distribution-free binary classification: prediction sets, confidence intervals and calibrationCode1
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance AwarenessCode1
Conformal Inference of Counterfactuals and Individual Treatment EffectsCode1
Uncertainty quantification in medical image segmentation with normalizing flowsCode1
Unsupervised Quality Estimation for Neural Machine TranslationCode1
Uncertainty Quantification Using Neural Networks for Molecular Property PredictionCode1
Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning AlgorithmsCode1
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterizationCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approachCode1
Uncertainty Estimation Using a Single Deep Deterministic Neural NetworkCode1
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong LearningCode1
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representationsCode1
Training Normalizing Flows with the Information Bottleneck for Competitive Generative ClassificationCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Stochastic Optimal Control as Approximate Input InferenceCode1
Deep Evidential RegressionCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Deep learning observables in computational fluid dynamicsCode1
Deep active subspaces - a scalable method for high-dimensional uncertainty propagationCode1
A Simple Baseline for Bayesian Uncertainty in Deep LearningCode1
Evidential Deep Learning to Quantify Classification UncertaintyCode1
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantificationCode1
Finite-dimensional Gaussian approximation with linear inequality constraintsCode1
Simple and Scalable Predictive Uncertainty Estimation using Deep EnsemblesCode1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
Distributional Reinforcement Learning on Path-dependent Options0
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