sremes / nonstationary-spectral-kernels
Implementation for Non-stationary Spectral Kernels (NIPS 2017)
☆20Updated 4 years ago
Related projects: ⓘ
- Non-stationary spectral mixture kernels implemented in GPflow☆27Updated 5 years ago
- Python and MATLAB code for Stein Variational sampling methods☆23Updated 5 years ago
- Code for ICML 2019 paper on "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"☆17Updated 3 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 3 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated 7 months ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 5 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆43Updated 6 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆23Updated last year
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆63Updated 5 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆36Updated 7 years ago
- Implementation of the Gaussian Process Autoregressive Regression Model☆59Updated 3 years ago
- Library for Deep Gaussian Processes based on GPflow☆19Updated 4 years ago
- ☆12Updated last year
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆28Updated 2 months ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆23Updated 5 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 5 years ago
- Continual Gaussian Processes☆30Updated last year
- Code required to reproduce the experiments in Auxiliary Variational MCMC☆17Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 4 years ago
- ☆28Updated 5 years ago
- Methods and experiments for assumed density SDE approximations☆11Updated 2 years ago
- Unifying sparse approximations for Gaussian process regression and classification, using Power EP☆21Updated 7 years ago
- Sequential Neural Likelihood☆38Updated 5 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆37Updated last month
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- ☆39Updated 5 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆38Updated 3 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 4 years ago
- A collection of Gaussian process models☆29Updated 7 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆38Updated 5 years ago