Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules-Reference-Cited by-同舟云学术

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Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules

Published:2020 Issue: Volume: Page:477-488
ISSN:0302-9743
Container-title:Machine Learning, Optimization, and Data Science
language:
Short-container-title:

Author:

Fraccaroli Michele^ORCID,Lamma Evelina^ORCID,Riguzzi Fabrizio^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-64583-0_43

Reference18 articles.

1. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);M Alberti,2016

2. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)

3. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)

4. Wikimedia Commons: File:gpparbayesanimationsmall.gif – wikimedia commons, the free media repository (2019). https://commons.wikimedia.org/w/index.php?title=File:GpParBayesAnimationSmall.gif&oldid=380025760. Accessed 16 Jan 2020

5. Dewancker, I., McCourt, M., Clark, S.: Bayesian optimization primer (2015)

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