Project details for Aboleth

Logo Aboleth 0.6.2

by dsteinberg - October 13, 2017, 01:21:35 CET [ Project Homepage BibTeX Download ]

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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.

Changes to previous version:

Hotfix release

  • fix random seeds
  • fix dropout sampling layers
BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux
Data Formats: Any
Tags: Deep Learning, Variational Inference, Gaussian Process, Tensorflow
Archive: download here

Other available revisons

Version Changelog Date
0.6.2

Hotfix release

  • fix random seeds
  • fix dropout sampling layers
October 13, 2017, 01:21:35
0.6

Some moderate changes to the API from:

  • Using TensorFlow's tf.distributions to replace Aboleth's likelihoods
  • Using TensorFlow's tf.distributions to replace Aboleth's distributions
September 27, 2017, 10:12:09
0.5

Initial Announcement on mloss.org.

September 7, 2017, 03:57:39

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