Machine Learning

Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions

This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.

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

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 …

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

Variational Bayes for Implicit Probabilistic Models

Primary PhD research topic---expanding the scope and applicability of *variational inference* to encompass *implicit probabilistic models*.

A Review of Implicit Models in Approximate Bayesian Inference

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.