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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:
Initial Announcement on mloss.org.
- 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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