Machine Learning

Building Probability Distributions with the TensorFlow Probability Bijector API

We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.

Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

A Review of Implicit Models in Approximate Bayesian Inference


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


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

Variational Bayes for Implicit Probabilistic Models

Primary PhD research topic: Expanding the scope and applicability of variational inference to encompass implicit probabilistic models.

A Tutorial on Variational Autoencoders with a Concise Keras Implementation

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


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