| Distributional Bayesian optimisation for variational inference on black-box simulators | Oct 16, 2019 | Bayesian OptimisationVariational Inference | CodeCode Available | 0 | 5 |
| Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models | Jun 7, 2023 | Hyperparameter OptimizationVariational Inference | CodeCode Available | 0 | 5 |
| Dep-L_0: Improving L_0-based Network Sparsification via Dependency Modeling | Jun 30, 2021 | Network PruningVariational Inference | CodeCode Available | 0 | 5 |
| Functional Gradient Flows for Constrained Sampling | Oct 30, 2024 | Variational Inference | CodeCode Available | 0 | 5 |
| Free-Form Variational Inference for Gaussian Process State-Space Models | Feb 20, 2023 | FormState Space Models | CodeCode Available | 0 | 5 |
| From Patches to Images: A Nonparametric Generative Model | Aug 1, 2017 | DenoisingImage Inpainting | CodeCode Available | 0 | 5 |
| Indian Buffet process for model selection in convolved multiple-output Gaussian processes | Mar 22, 2015 | Gaussian ProcessesModel Selection | CodeCode Available | 0 | 5 |
| A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms | Nov 8, 2018 | Bayesian Inferenceparameter estimation | CodeCode Available | 0 | 5 |
| Flexible Tails for Normalizing Flows | Jun 22, 2024 | Density EstimationVariational Inference | CodeCode Available | 0 | 5 |
| Functional Variational Bayesian Neural Networks | Mar 14, 2019 | Bayesian InferenceGaussian Processes | CodeCode Available | 0 | 5 |
| Input-gradient space particle inference for neural network ensembles | Jun 5, 2023 | DiversityEnsemble Learning | CodeCode Available | 0 | 5 |
| Deep Variational Implicit Processes | Jun 14, 2022 | Gaussian ProcessesVariational Inference | CodeCode Available | 0 | 5 |
| Interpolating between sampling and variational inference with infinite stochastic mixtures | Oct 18, 2021 | Variational Inference | CodeCode Available | 0 | 5 |
| Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data | May 20, 2016 | State Space ModelsVariational Inference | CodeCode Available | 0 | 5 |
| Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning | Dec 5, 2024 | Computational EfficiencyDeep Learning | CodeCode Available | 0 | 5 |
| Addressing Catastrophic Forgetting in Few-Shot Problems | Apr 30, 2020 | ClassificationGeneral Classification | CodeCode Available | 0 | 5 |
| Doubly Stochastic Variational Inference for Deep Gaussian Processes | May 24, 2017 | Gaussian ProcessesGeneral Classification | CodeCode Available | 0 | 5 |
| Approximate Inference for Fully Bayesian Gaussian Process Regression | Dec 31, 2019 | GPRregression | CodeCode Available | 0 | 5 |
| Knowing what you know in brain segmentation using Bayesian deep neural networks | Dec 3, 2018 | Brain SegmentationVariational Inference | CodeCode Available | 0 | 5 |
| Laplace Matching for fast Approximate Inference in Latent Gaussian Models | May 7, 2021 | Bayesian InferenceGaussian Processes | CodeCode Available | 0 | 5 |
| Finding Convincing Arguments Using Scalable Bayesian Preference Learning | Jun 6, 2018 | Active LearningVariational Inference | CodeCode Available | 0 | 5 |
| Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0 | Aug 23, 2023 | Benchmarkingregression | CodeCode Available | 0 | 5 |
| Accelerating Convergence in Bayesian Few-Shot Classification | May 2, 2024 | ClassificationFew-Shot Learning | CodeCode Available | 0 | 5 |
| DropMax: Adaptive Variational Softmax | Dec 21, 2017 | ClassificationGeneral Classification | CodeCode Available | 0 | 5 |
| Flexible Amortized Variational Inference in qBOLD MRI | Mar 11, 2022 | Bayesian InferenceUncertainty Quantification | CodeCode Available | 0 | 5 |
