akuhren / selective_gp
Code accompanying the paper "Probabilistic Selection of Inducing Points in Sparse Gaussian Processes".
☆24Updated 2 years ago
Alternatives and similar repositories for selective_gp:
Users that are interested in selective_gp are comparing it to the libraries listed below
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆80Updated 4 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆17Updated 3 years ago
- ☆15Updated 2 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆23Updated 2 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆41Updated 6 months ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆81Updated 7 months ago
- Gaussian Processes for Sequential Data☆18Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆111Updated 3 years ago
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 2 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆64Updated 5 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆38Updated 3 years ago
- Library for Deep Gaussian Processes based on GPflow☆19Updated 4 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆69Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆55Updated 3 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Python and MATLAB code for Stein Variational sampling methods☆24Updated 5 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated 11 months ago
- ☆28Updated 5 years ago
- MIGSAA Project 2 - Langevin Monte Carlo Algorithms☆12Updated last year
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆148Updated 5 years ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆29Updated 3 years ago
- Gaussian processes with PyTorch☆30Updated 3 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆123Updated 3 months ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆88Updated 4 years ago