| Factorized Fusion Shrinkage for Dynamic Relational Data | Sep 30, 2022 | Variational Inference | CodeCode Available | 0 | 5 |
| A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime | Feb 11, 2019 | Small Data Image ClassificationUncertainty Quantification | CodeCode Available | 0 | 5 |
| Bayesian Neural Networks With Maximum Mean Discrepancy Regularization | Mar 2, 2020 | image-classificationImage Classification | CodeCode Available | 0 | 5 |
| Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization | Oct 25, 2023 | Bayesian OptimizationVariational Inference | CodeCode Available | 0 | 5 |
| Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study | Feb 2, 2021 | Bayesian InferenceVariational Inference | CodeCode Available | 0 | 5 |
| An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks | Jun 25, 2019 | Variational Inference | CodeCode Available | 0 | 5 |
| Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization | Dec 8, 2020 | Density EstimationVariational Inference | CodeCode Available | 0 | 5 |
| A Flexible Generative Framework for Graph-based Semi-supervised Learning | May 26, 2019 | Missing LabelsVariational Inference | CodeCode Available | 0 | 5 |
| Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming | Sep 30, 2018 | Probabilistic ProgrammingRepresentation Learning | CodeCode Available | 0 | 5 |
| Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam | Jun 13, 2018 | Reinforcement LearningStochastic Optimization | CodeCode Available | 0 | 5 |
