idelbrid / Randomly-Projected-Additive-GPs
Code for Randomly Projected Additive Gaussian Processes
☆25Updated 5 years ago
Alternatives and similar repositories for Randomly-Projected-Additive-GPs:
Users that are interested in Randomly-Projected-Additive-GPs are comparing it to the libraries listed below
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 3 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆80Updated 4 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆81Updated 7 months ago
- Gaussian processes with PyTorch☆30Updated 3 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆64Updated 5 years ago
- ☆28Updated 5 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Sequential Neural Likelihood☆38Updated 5 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- Library for Deep Gaussian Processes based on GPflow☆19Updated 4 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆21Updated 5 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆48Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated 11 months ago
- A variational method for fast, approximate inference for stochastic differential equations.☆43Updated 6 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆40Updated 5 months ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 4 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 4 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆148Updated 5 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆38Updated 5 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆23Updated 2 years ago
- The code for Meta Learning for SGMCMC☆24Updated 5 years ago
- Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)☆39Updated 7 years ago
- Hierarchical Change-Point Detection☆14Updated 6 years ago
- Understanding normalizing flows☆131Updated 5 years ago