| Adversarial Uncertainty Quantification in Physics-Informed Neural Networks | Nov 9, 2018 | Uncertainty Quantification | CodeCode Available | 0 |
| Uncertainty in Neural Networks: Approximately Bayesian Ensembling | Oct 12, 2018 | Bayesian InferenceGeneral Classification | CodeCode Available | 0 |
| Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems | Sep 21, 2018 | Active LearningUncertainty Quantification | —Unverified | 0 |
| Data-driven polynomial chaos expansion for machine learning regression | Aug 9, 2018 | BIG-bench Machine Learningregression | —Unverified | 0 |
| Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors | Jul 30, 2018 | Medical Image AnalysisProbabilistic Deep Learning | —Unverified | 0 |
| Estimating Failure in Brittle Materials using Graph Theory | Jul 30, 2018 | Uncertainty Quantification | —Unverified | 0 |
| Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization | Jul 23, 2018 | Bayesian OptimizationSimultaneous Localization and Mapping | —Unverified | 0 |
| Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees | Jul 10, 2018 | continuous-controlContinuous Control | CodeCode Available | 0 |
| Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks | Jul 8, 2018 | FormUncertainty Quantification | CodeCode Available | 0 |
| Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media | Jul 2, 2018 | Computational EfficiencyDecoder | CodeCode Available | 0 |
