Randomly Projected Additive Gps
Code for Randomly Projected Additive Gaussian Processes
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Gpflow | 1,783 |  | 17 | 19 | 4 months ago | 39 | August 09, 2023 | 149 | apache-2.0 | Python |
| Gaussian processes in TensorFlow | ||||||||||
| Stheno | 207 |  | 4 | 4 months ago | 38 | October 22, 2022 | 4 | mit | Python | |
| Gaussian process modelling in Python | ||||||||||
| Abstractgps.jl | 200 | 5 months ago | 37 | other | Julia | |||||
| Abstract types and methods for Gaussian Processes. | ||||||||||
| Augmentedgaussianprocesses.jl | 132 | a year ago | 21 | other | Julia | |||||
| Gaussian Process package based on data augmentation, sparsity and natural gradients | ||||||||||
| Gp_model_zoo | 20 | 3 years ago | mit | Jupyter Notebook | ||||||
| Literature and light wrappers for gaussian process models. | ||||||||||
| Randomly Projected Additive Gps | 10 | 4 years ago | Python | |||||||
| Code for Randomly Projected Additive Gaussian Processes | ||||||||||
| Gp Predictit Blog | 9 | 7 years ago | R | |||||||
| Code for blog posts on Gaussian processes, including betting on PredictIt with GPs | ||||||||||
| Gp | 8 | 7 years ago | other | C++ | ||||||
| A lightweight C++ library for Gaussian processes (GPs). | ||||||||||
| Doubly Stochastic Deep Gaussian Process | 5 | 4 years ago | 1 | apache-2.0 | Python | |||||
| Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression. | ||||||||||
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Python
Gps
Gaussian Processes