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GPflow

@GPflowProject

Gaussian Processes in TensorFlow! site: https://xitter.gq/t.co/D4vly0ASH5 gh: https://xitter.gq/t.co/8SVShaZ9Ez

 Bayesian Paradise
Joined July 2020
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A tutorial on Gaussian Process Regression for molecules using GPflow! medium.com/@ryangriff123/gau… @GPflowProject
Image from Ryan-Rhys Griffiths (@Ryan__Rhys)
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Here is a visual of how samples from a Gaussian process update in light of new data. Made with @manim_community
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Our tutorial on sparse Gaussian processes (GPs) and variational inference (VI) is online: arxiv.org/abs/2012.13962 covering interdomain, multioutput and deep GPs, as well as importance-weighted VI, but also VI with neural networks! @vdutor @scien_ti_st @NicolasDurrande
A Tutorial on Sparse Gaussian Processes and Variational Inference

A Tutorial on Sparse Gaussian Processes and Variational Inference

Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regression problems...

arxiv.org
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What is Trieste? We recently launched our open-source toolbox: Trieste. It's a Python library for Bayesian optimization, based on @TensorFlow and @GPflowProject secondmind.ai/labs/trieste-o…
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