GPflux: A Library for Deep Gaussian Processes

by   Vincent Dutordoir, et al.

We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the various mathematical subtleties that arise when dealing with multivariate Gaussian distributions and the complex bookkeeping of indices. To date, there are no actively maintained, open-sourced and extendable libraries available that support research activities in this area. GPflux aims to fill this gap by providing a library with state-of-the-art DGP algorithms, as well as building blocks for implementing novel Bayesian and GP-based hierarchical models and inference schemes. GPflux is compatible with and built on top of the Keras deep learning eco-system. This enables practitioners to leverage tools from the deep learning community for building and training customised Bayesian models, and create hierarchical models that consist of Bayesian and standard neural network layers in a single coherent framework. GPflux relies on GPflow for most of its GP objects and operations, which makes it an efficient, modular and extensible library, while having a lean codebase.



There are no comments yet.


page 1

page 2

page 3

page 4


Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework

A software library for constructing and learning probabilistic models is...

Deep Gaussian Processes for geophysical parameter retrieval

This paper introduces deep Gaussian processes (DGPs) for geophysical par...

Translation Insensitivity for Deep Convolutional Gaussian Processes

Deep learning has been at the foundation of large improvements in image ...

Probabilistic Programming with Gaussian Process Memoization

Gaussian Processes (GPs) are widely used tools in statistics, machine le...

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applicati...

Recurrent Gaussian Processes

We define Recurrent Gaussian Processes (RGP) models, a general family of...

MOGPTK: The Multi-Output Gaussian Process Toolkit

We present MOGPTK, a Python package for multi-channel data modelling usi...

Code Repositories


Deep GPs built on top of TensorFlow/Keras and GPflow

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.