Project details for Aboleth

Logo Aboleth 0.7

by dsteinberg - December 14, 2017, 02:39:19 CET [ Project Homepage BibTeX Download ]

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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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