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GPflow: A Gaussian process library using TensorFlow

2016-10-27 · Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman

GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.

📄 PDF Abstract BibTeX arXiv:1610.08733

Code (1)

GPflow/GPflow tf

Tasks

Gaussian ProcessesGPUsoftware testingVariational Inference

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Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

2023-02-16 · Victor Picheny, Joel Berkeley, Henry B. Moss, Hrvoje Stojic 외

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GPRat: Gaussian Process Regression with Asynchronous Tasks

2025-04-30 · Maksim Helmann, Alexander Strack, Dirk Pflüger

Python is the de-facto language for software development in artificial intelligence (AI). Commonly used libraries, such as PyTorch and TensorFlow, rely on parallelization built into their BLAS backends to achieve speedup…

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Gaussian Process Molecule Property Prediction with FlowMO

2020-10-02 · Henry B. Moss, Ryan-Rhys Griffiths

We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make predictions with well-calibrated uncertainty estima…

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GPflux: A Library for Deep Gaussian Processes

2021-04-12 · Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod 외

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 aris…

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