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Software . 2019
License: https://www.gnu.org/licenses/gpl-3.0-standalone.html
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IGITUGraz/L2L: v1.0.0-beta

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 11 Mar 2019 English Publisher:Zenodo
Authors: Subramoney, Anand; Diaz-Pier, Sandra; Rao, Arjun; Scherr, Franz; Salaj, Darjan; Bohnstingl, Thomas; Jordan, Jakob; +3 Authors
Code repository: https://github.com/IGITUGraz/L2L/tree/v1.0.0-beta

doi: 10.5281/zenodo.2553955 , 10.5281/zenodo.2590760

IGITUGraz/L2L: v1.0.0-beta

Abstract

The L2L (Learning-to-learn) gradient-free optimization framework contains well documented and tested implementations of various gradient free optimization algorithms. It also defines an API that makes it easy to optimize (hyper-)parameters for any task (optimizee). All the implementations in this package are parallel and can run across different cores and nodes (but equally well on a single core). It includes integration with the JUBE framework, which allows running this framework on HPC clusters.

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Keywords

machine learning, meta-learning, optimization

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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.
BIP!Impulse provided by BIP!
views
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1
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