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Software . 2022
License: CC BY
Data sources: Datacite
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simpleSVGD: A tiny interface to Stein Variational Gradient Descent using various optimization algorithms

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 31 Jan 2022 English Publisher:Zenodo
Authors: Gebraad, LARS;

doi: 10.5281/zenodo.5938429 , 10.5281/zenodo.5938430

simpleSVGD: A tiny interface to Stein Variational Gradient Descent using various optimization algorithms

Abstract

This Python package is a tiny Stein Variational Gradient Descent (SVGD) algorithm specifically developed to operate on distributions found in HMCLab. By default, this package uses radial basis functions to compute sample interaction and gradient descent to optimise the ensemble of samples. An interface is supplied to use any optimization algorithm in Torch. The code is an evolution of the original implementation of the SVGD algorithm. Installation using pip: pip install simpleSVGD

Related Organizations
Keywords

Optimization, Bayesian Inference, Probabilistic Inversion, Python

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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