Release Version 0.3.1

• Added SVGP regression notebook.
• Updated the VAE and GP regression notebook.
• Removed the dependency on scikit-learn.
• Moved the parameters of Gluon block to be controlled by MXFusion.
• Fixed a bug in mini-batch learning.
• Extended the SVGPRegression module to take samples as input variables.
• Documentation and stylistic edits.
• Merged in the PILCO Changes

Release Version 0.3.0

The bigger changes are:

• #133 Changing variable broadcasting for factors
• #153 Simplify serialization to use a simple zip file

Other than that its mostly bug fixes and documentation changes.

• #144 Add details to inference and serialization documentation
• #148 Fix a bug for SVGP regression with minibatch traning
• #81 Reduce num_samples in uniform non-mock test to 1000
• #137 Changed transpose property to function.
• #135 Bug fix in function evaluation

Release Version 0.2.1

• Add the tutorial for Gaussian process regression.
• Fix empty operator bug.
• Fix bug to allow the same variable for multiple inputs to a factor.
• Add module serialization.
• Fix the bug that causes the failure of documentation compilation.
• Fix the bug: the inference methods of GP modules do not handle samples.
• Update issue templates.
• Add license headers to all files.
• Add the getting started notebook.
• Remove the dependency on future.
• Update the Inference documentation.
• Implement Dirichlet distribution.
• Add logistic variable transformation.
• Implement Expectation inference.
• Fix the bugs related to dtype.
• Validate shape of array variables.
• Fix divide by zero error if max_iter < n_prints.
• Add multiply kernel.

Release version 0.2.0

• Improve the printing messages of gradient loop and bug fix for GluonFunctionEvaluation
• Add Gamma Distribution
• GP Modules enhancement
• Implement Module, GPRegression, SparseGPRegression and SVGPRegression…
• Update the README
• Add score function for variational inference enhancement
• Refactor the interface of inferece for module
• Add score function inference
• Implement common MXNet operators (dot product, diag, etc. in MXFusion enhancement
• Add support for MXNet operators.
• Cleanup kernel function and function wrappers for copying enhancement
• Implement the base class MXFusionFunction