Machine Learning

Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from …

Variational Bayes for Implicit Probabilistic Models

Expanding the scope and applicability of variational inference to encompass implicit probabilistic models.

Aboleth

Aboleth is a minimalistic TensorFlow framework for scalable Bayesian deep learning and Gaussian process approximation.

Determinant

Determinant is a software service that makes predictions from sparse data, and learns what data it needs to optimise its performance.

Revrand

Revrand is a full-featured Python library for Bayesian generalized linear models, with random basis kernels for large-scale Gaussian process approximations.

A Tutorial on Variational Autoencoders with a Concise Keras Implementation

An in-depth practical guide to variational encoders from a probabilistic perspective.