Primarily, Aboleth is much reduced in scope compared to frameworks like Edward and ZhuSuan; we really only concentrate on neural net-like structures (though you can construct a lot of non-neural net models with these too), and we have only implemented SGVB and "MAP" inference. We have slightly more of a "production" focus than a research focus too (though we would love people to use Aboleth for their research) - for instance we have efficient categorical feature embedding and imputation input layers, where all of the parameters of these layers are learned with the rest of the model. Because we have a limited scope it allows for a nice clean interface and minimal abstraction over pure TensorFlow. Really, all we have done in Aboleth is implement a writer monad, i.e. function composition (the neural net) with an accumulate (the complexity penalties of the layers). This structure is easy to use, extend, and test!

The purpose of Aboleth is to provide a set of high performance and light weight components for building Bayesian neural nets and approximate (deep) Gaussian process computational graphs (and more). We aim for minimal abstraction over pure TensorFlow, so you can easily assign parts of the computational graph to different hardware, use your own data feeds/queues, and manage your own sessions etc. Have a look at the docs for more information!