Project details for Aboleth

Logo Aboleth 0.5

by dsteinberg - September 7, 2017, 03:57:39 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.

Changes to previous version:

Initial Announcement on

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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