| The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks | Jan 7, 2021 | Adversarial RobustnessVariational Inference | —Unverified | 0 | 0 |
| The equivalence between Stein variational gradient descent and black-box variational inference | Apr 4, 2020 | Bayesian InferenceVariational Inference | —Unverified | 0 | 0 |
| The Free Energy Principle for Perception and Action: A Deep Learning Perspective | Jul 13, 2022 | Deep LearningVariational Inference | —Unverified | 0 | 0 |
| The Gaussian Process Latent Autoregressive Model | Nov 23, 2020 | DenoisingGaussian Processes | —Unverified | 0 | 0 |
| The Generalized Reparameterization Gradient | Oct 7, 2016 | Variational Inference | —Unverified | 0 | 0 |
| The Generalized Reparameterization Gradient | Dec 1, 2016 | Variational Inference | —Unverified | 0 | 0 |
| The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks | Feb 7, 2020 | Bayesian InferenceVariational Inference | —Unverified | 0 | 0 |
| Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection | Oct 30, 2017 | Bayesian InferenceCommunity Detection | —Unverified | 0 | 0 |
| Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA | Mar 25, 2022 | Dimensionality ReductionVariational Inference | —Unverified | 0 | 0 |
| Theoretical guarantees for sampling and inference in generative models with latent diffusions | Mar 5, 2019 | Variational Inference | —Unverified | 0 | 0 |
