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| The Unreasonable Effectiveness of Deep Evidential Regression | May 20, 2022 | regressionUncertainty Quantification | CodeCode Available | 1 |
| Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networks | May 19, 2022 | Uncertainty Quantification | —Unverified | 0 |
| Achieving Risk Control in Online Learning Settings | May 18, 2022 | Conformal PredictionDepth Estimation | CodeCode Available | 0 |
| Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels | May 18, 2022 | Gaussian ProcessesUncertainty Quantification | —Unverified | 0 |
| Finite Element Method-enhanced Neural Network for Forward and Inverse Problems | May 17, 2022 | Uncertainty Quantification | —Unverified | 0 |
| Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data | May 14, 2022 | Uncertainty Quantification | —Unverified | 0 |
| A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification | May 13, 2022 | GPRregression | —Unverified | 0 |
| Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems | May 12, 2022 | Bayesian InferenceModel Selection | —Unverified | 0 |
| Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning | May 12, 2022 | Gaussian ProcessesUncertainty Quantification | CodeCode Available | 0 |
