| Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference | Jan 1, 2018 | Computational EfficiencyVariational Inference | CodeCode Available | 0 | 5 |
| Training Bayesian Neural Networks with Sparse Subspace Variational Inference | Feb 16, 2024 | Uncertainty QuantificationVariational Inference | CodeCode Available | 0 | 5 |
| Coherence-Aware Neural Topic Modeling | Sep 7, 2018 | Topic ModelsVariational Inference | CodeCode Available | 0 | 5 |
| Training Robust Graph Neural Networks by Modeling Noise Dependencies | Feb 27, 2025 | Variational Inference | CodeCode Available | 0 | 5 |
| Generalized Variational Inference: Three arguments for deriving new Posteriors | Apr 3, 2019 | Bayesian InferenceGaussian Processes | CodeCode Available | 0 | 5 |
| Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure | Nov 22, 2019 | Bayesian InferenceNeural Architecture Search | CodeCode Available | 0 | 5 |
| Gaussian Process-Gated Hierarchical Mixtures of Experts | Feb 9, 2023 | Gaussian ProcessesVariational Inference | CodeCode Available | 0 | 5 |
| Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates | Jan 26, 2023 | Bayesian InferenceVariational Inference | CodeCode Available | 0 | 5 |
| Analyses of Multi-collection Corpora via Compound Topic Modeling | Jun 17, 2019 | Topic ModelsVariational Inference | CodeCode Available | 0 | 5 |
| Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning | May 12, 2022 | Gaussian ProcessesUncertainty Quantification | CodeCode Available | 0 | 5 |
