AaltoML / boundary-gp
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
☆23Updated 6 years ago
Alternatives and similar repositories for boundary-gp
Users that are interested in boundary-gp are comparing it to the libraries listed below
Sorting:
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 6 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 for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 5 years ago
- Recyclable Gaussian Processes☆11Updated 2 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 5 years ago
- Train neural networks to use as SMC and importance sampling proposals☆24Updated 7 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆29Updated 3 months ago
- A collection of Gaussian process models☆30Updated 7 years ago
- ☆28Updated 6 years ago
- Code to minimize the Variational Contrastive Divergence (VCD)☆29Updated 5 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Python implementation of the PR-SSM.☆51Updated 6 years ago
- ☆11Updated 8 years ago
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆37Updated 7 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- Variational Auto-Regressive Gaussian Processes for Continual Learning☆21Updated 3 years ago
- Modular Gaussian Processes☆15Updated 3 years ago
- Python and MATLAB code for Stein Variational sampling methods☆25Updated 5 years ago
- ☆30Updated 2 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- Sequential Neural Likelihood☆40Updated 5 years ago
- a deep recurrent model for exchangeable data☆34Updated 4 years ago
- ☆40Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago