Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

jelena luketina
jelena luketina

ICML, 2016.

Cited by: 34|Bibtex|Views29|Links
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Abstract:

Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance. We propose a gradient-based approach for locally adjusting hyperparameters during training of the model. Hyperparameters are adjusted so as to make the model parameter gradients, and hence updates, more a...More

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