Description:

A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation with stochastic gradient variational Bayes inference.

Some of the features of Aboleth:

• Bayesian fully-connected, embedding and convolutional layers using SGVB for inference.

• Random Fourier and arc-cosine features for approximate Gaussian processes. Optional variational optimisation of these feature weights.

• Imputation layers with parameters that are learned as part of a model.

• Very flexible construction of networks, e.g. multiple inputs, ResNets etc.

• Optional maximum-likelihood type II inference for model parameters such as weight priors/regularizers and regression observation noise.

• Compatible and interoperable with other neural net frameworks such as Keras (see the demos for more information).

Changes to previous version:

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.

BibTeX Entry: Download

Supported Operating Systems: Linux

Data Formats: Any

Tags: Deep Learning, Variational Inference, Gaussian Process, Tensorflow

Archive: download here

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