| Modified Frank Wolfe in Probability Space | Dec 1, 2021 | Variational Inference | —Unverified | 0 |
| Functional Variational Inference based on Stochastic Process Generators | Dec 1, 2021 | Bayesian InferenceVariational Inference | —Unverified | 0 |
| Scalable Bayesian GPFA with automatic relevance determination and discrete noise models | Dec 1, 2021 | Variational Inference | —Unverified | 0 |
| Learning to Learn Dense Gaussian Processes for Few-Shot Learning | Dec 1, 2021 | Few-Shot LearningGaussian Processes | —Unverified | 0 |
| EDGE: Explaining Deep Reinforcement Learning Policies | Dec 1, 2021 | Deep Reinforcement LearningMuJoCo | CodeCode Available | 1 |
| Latent Matters: Learning Deep State-Space Models | Dec 1, 2021 | State Space ModelsVariational Inference | —Unverified | 0 |
| Joint Modeling of Visual Objects and Relations for Scene Graph Generation | Dec 1, 2021 | Graph EmbeddingGraph Generation | —Unverified | 0 |
| Optimality of variational inference for stochasticblock model with missing links | Dec 1, 2021 | parameter estimationStochastic Block Model | —Unverified | 0 |
| Variational Continual Bayesian Meta-Learning | Dec 1, 2021 | Meta-LearningTransfer Learning | —Unverified | 0 |
| Topic Modeling Revisited: A Document Graph-based Neural Network Perspective | Dec 1, 2021 | Variational Inference | CodeCode Available | 1 |
