otokonoko8 / deep-Bayesian-nonparametrics-papersLinks
The collection of papers about combining deep learning and Bayesian nonparametrics
☆122Updated 6 years ago
Alternatives and similar repositories for deep-Bayesian-nonparametrics-papers
Users that are interested in deep-Bayesian-nonparametrics-papers are comparing it to the libraries listed below
Sorting:
- The collection of recent papers about variational inference☆84Updated 6 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- A community repository for benchmarking Bayesian methods☆112Updated 4 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆101Updated 7 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆42Updated 3 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Understanding normalizing flows☆132Updated 6 years ago
- Deep neural network kernel for Gaussian process☆212Updated 5 years ago
- Papers for Bayesian-NN☆326Updated 6 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated last year
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆49Updated 5 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆151Updated 6 years ago
- ☆29Updated 6 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)☆238Updated 7 years ago
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Pytorch implementation of Neural Processes for functions and images☆234Updated 3 years ago
- Code to minimize the Variational Contrastive Divergence (VCD)☆29Updated 6 years ago
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆94Updated 4 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆93Updated 5 years ago
- Deep convolutional gaussian processes.☆82Updated 6 years ago
- ☆172Updated last year
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Package implementing various parametric and nonparametric methods for conditional density estimation☆197Updated 2 years ago
- a repo sharing Bayesian Neural Network recent papers☆216Updated 6 years ago
- PyTorch implementation of Neural Processes☆88Updated 6 years ago