Abolethhttp://mloss.orgUpdates and additions to AbolethenThu, 14 Dec 2017 02:39:19 -0000Aboleth 0.7<html><p>A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation with stochastic gradient variational Bayes inference. </p> <p>Some of the features of Aboleth: </p> <ul> <li><p>Bayesian fully-connected, embedding and convolutional layers using SGVB for inference. </p> </li> <li><p>Random Fourier and arc-cosine features for approximate Gaussian processes. Optional variational optimisation of these feature weights. </p> </li> <li><p>Imputation layers with parameters that are learned as part of a model. </p> </li> <li><p>Very flexible construction of networks, e.g. multiple inputs, ResNets etc. </p> </li> <li><p>Optional maximum-likelihood type II inference for model parameters such as weight priors/regularizers and regression observation noise. </p> </li> <li><p>Compatible and interoperable with other neural net frameworks such as Keras (see the demos for more information). </p> </li> </ul></html>daniel steinberg, lachlan mccalman, louis tiao, , simon ocallaghan, alistair reidThu, 14 Dec 2017 02:39:19 -0000 learningvariational inferencegaussian processtensorflow