thjashin / solvegpLinks
Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)
☆22Updated 4 years ago
Alternatives and similar repositories for solvegp
Users that are interested in solvegp are comparing it to the libraries listed below
Sorting:
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆38Updated 4 years ago
- ☆40Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 4 years ago
- Variational Fourier Features☆85Updated 4 years ago
- Deep convolutional gaussian processes.☆80Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 7 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆93Updated 3 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 4 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Understanding normalizing flows☆132Updated 5 years ago
- ☆29Updated 6 years ago
- ☆59Updated 6 years ago
- Neural Processes implementation for 1D regression☆64Updated 6 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 5 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- ☆170Updated last year
- ☆25Updated 7 years ago
- The code for Meta Learning for SGMCMC☆25Updated 6 years ago