Hyperparameter optimization: Difference between revisions

Revision as of 18:31, 9 March 2018

Revision as of 20:50, 10 March 2018

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Evolutionary optimization has been used in hyperparameter optimization for statistical machine learning algorithms<ref name="bergstra11" />, [[automated machine learning]]<ref name="tpot1" /><ref name="tpot2" />, [[Deep_learning#Deep_neural_networks|deep neural network]] architecture search<ref name="miikkulainen1">{{cite arxiv | vauthors = Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N, Hodjat B | year = 2017 | title = Evolving Deep Neural Networks |eprint=1703.00548| class = cs.NE }}</ref><ref name="jaderberg1">{{cite arxiv | vauthors = Jaderberg M, Dalibard V, Osindero S, Czarnecki WM, Donahue J, Razavi A, Vinyals O, Green T, Dunning I, Simonyan K, Fernando C, Kavukcuoglu K | year = 2017 | title = Population Based Training of Neural Networks |eprint=1711.09846| class = cs.LG }}</ref>, as well as training of the weights in deep neural networks<ref name="such1">{{cite arxiv | vauthors = Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J | year = 2017 | title = Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning |eprint=1712.06567| class = cs.NE }}</ref>.

In another vein, recent work studied the hyperparameter space of [[Evolutionary algorithm]]s, concluding that this space tends to be rife with viable parameters.<ref>M. Sipper, W. Fu, K. Ahuja, and J. H. Moore, [https://biodatamining.biomedcentral.com/articles/10.1186/s13040-018-0164-x Investigating the Parameter Space of Evolutionary Algorithms], BioData Mining, 11:2, 2018. </ref>

=== Others ===