About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Release 0.7.0

• Update to TensorFlow r1.4.

• Tutorials in the documentation on:

• Interfacing with Keras

• Saving/loading models

• How to build a variety of regressors with Aboleth

• New prediction module with some convenience functions, including freezing the weight samples during prediction.

• Bayesian convolutional layers with accompanying demo.

• Allow the number of samples drawn from a model to be varied by using placeholders.

• Generalise the feature embedding layers to work on matrix inputs (instead of just column vectors).

• Numerous numerical and usability fixes.

About: A library of scalable Bayesian generalised linear models with fancy features

• 1.0 release!
• Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
• Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
• Random state can be set for all random objects
• Numerous small improvements to make revrand production ready
• Final report
• Documentation improvements

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.

Initial Announcement on mloss.org.