| Fully Nonparametric Bayesian Additive Regression Trees | Jun 29, 2018 | regressionUncertainty Quantification | —Unverified | 0 |
| Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting | Jun 28, 2018 | Spatio-Temporal ForecastingUncertainty Quantification | —Unverified | 0 |
| Neural-net-induced Gaussian process regression for function approximation and PDE solution | Jun 22, 2018 | Gaussian Processesregression | —Unverified | 0 |
| A data-driven model order reduction approach for Stokes flow through random porous media | Jun 21, 2018 | Uncertainty Quantification | —Unverified | 0 |
| Evidential Deep Learning to Quantify Classification Uncertainty | Jun 5, 2018 | Deep LearningGeneral Classification | CodeCode Available | 1 |
| Bayesian approach to model-based extrapolation of nuclear observables | Jun 1, 2018 | Gaussian ProcessesUncertainty Quantification | —Unverified | 0 |
| Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification | May 28, 2018 | ClassificationGaussian Processes | CodeCode Available | 0 |
| Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models | May 23, 2018 | Uncertainty Quantification | —Unverified | 0 |
| Deep Directional Statistics: Pose Estimation with Uncertainty Quantification | May 9, 2018 | Deep LearningPose Estimation | CodeCode Available | 0 |
| Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands | May 2, 2018 | Bayesian InferenceUncertainty Quantification | CodeCode Available | 0 |
