google / decoupled_gaussian_process
☆23Updated 6 years ago
Alternatives and similar repositories for decoupled_gaussian_process:
Users that are interested in decoupled_gaussian_process are comparing it to the libraries listed below
- Demonstration of Jackknife Variational Inference for Variational Autoencoders, related to ICLR 2018 paper.☆21Updated 6 years ago
- Deep Gaussian Processes in matlab☆91Updated 3 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆64Updated 5 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆38Updated 5 years ago
- Python implementation of the PR-SSM.☆51Updated 6 years ago
- Look Ahead Hamiltonian Monte Carlo☆30Updated 9 years ago
- Python and MATLAB code for Stein Variational sampling methods☆24Updated 5 years ago
- code supplement for variational boosting (https://arxiv.org/abs/1611.06585)☆11Updated 7 years ago
- Code for ICML 2019 paper on "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"☆18Updated 4 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆40Updated 8 years ago
- Black box variational inference for state space models☆1Updated 8 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 3 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Repo for a paper about constructing priors on very deep models.☆72Updated 8 years ago
- Implementation of stochastic variational inference for Bayesian hidden Markov models.☆65Updated 7 years ago
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated 2 years ago
- Variational Gaussian Process State-Space Models☆23Updated 9 years ago
- Unifying sparse approximations for Gaussian process regression and classification, using Power EP☆22Updated 8 years ago
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 10 years ago
- A collection of Gaussian process models☆30Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Train neural networks to use as SMC and importance sampling proposals☆24Updated 7 years ago
- Additive Gaussian Process Bandits - version 1.0☆26Updated 8 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆37Updated 7 years ago
- Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"☆25Updated 6 years ago
- ☆28Updated 5 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- ☆17Updated 6 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 7 years ago
- ☆40Updated 5 years ago