AutoLR: An Evolutionary Approach to Learning Rate Policies

Pedro Carvalho
Pedro Carvalho
Filipe Assunção
Filipe Assunção

genetic and evolutionary computation conference, pp. 672-680, 2020.

Cited by: 0|Bibtex|Views10|DOI:https://doi.org/10.1145/3377930.3390158
Other Links: arxiv.org|academic.microsoft.com

Abstract:

The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While the...More

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