Implementation of Gaussian Process Latent Variable models in pytorch/gpytorch
Key References:
- Gaussian process latent variable models for visualisation of high dimensional data
- Bayesian GPLVM
- Local distance preservation in the GPLVM
- Stochastic Variational Inference for back-constrained GPLVM
- Gaussian Processes for Big data
Models
- GPLVM
- B-GPLVM
- Back-constrained GPLVM
- SVI GPLVM (with and without back-constraints)
Inference
- ML-II Optimisation
- Variational Inference with the collapsed bound
- Stochastic Variational Inference with the uncollapsed bound
Code Layout
data/* has all the data loading utilities
models/* model classes
Usage
See demo.py