NGD notebook bookmark
@@ -23,6 +23,7 @@ The variational documentation has more information on how to use these objects.
Here we provide some examples which highlight some of the common use cases:
- **Large-scale regression** (when exact methods are too memory intensive): see the `stochastic variational regression example`_.
+- **Variational inference with natural gradient descent** (for faster/better optimization): see the `ngd example`_.
- **Variational distribution options** for different scalability/expressiveness: see the `strategy/distribution comparison`_.
- **Alternative optimization objectives** for the GP's predictive distribution: see the `approximate GP objective functions notebook`_.
This example compares and contrasts the variational ELBO with the predictive log likelihood of Jankowiak et al., 2020.
@@ -36,6 +37,7 @@ Here we provide some examples which highlight some of the common use cases:
SVGP_Regression_CUDA.ipynb
Modifying_the_variational_strategy_and_distribution.ipynb
+ Natural_Gradient_Descent.ipynb
Approximate_GP_Objective_Functions.ipynb
Non_Gaussian_Likelihoods.ipynb
SVGP_Multitask_GP_Regression.ipynb
@@ -47,6 +49,9 @@ Here we provide some examples which highlight some of the common use cases:
.. _stochastic variational regression example:
./SVGP_Regression_CUDA.ipynb
+.. _ngd example:
+ ./Natural_Gradient_Descent.ipynb
+
.. _approximate GP objective functions notebook:
./Approximate_GP_Objective_Functions.ipynb
