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Hyperparameter selection generally relies on running multiple full trainingtrials, with selection based on validation set performance. We propose agradient-based approach for locally adjusting hyperparameters during trainingof the model. Hyperparameters are adjusted so as to make the model parametergradients, and hence updates, more advantageous for the validation cost. Weexplore the approach for tuning regularization hyperparameters and find that inexperiments on MNIST, SVHN and CIFAR-10, the resulting regularization levelsare within the optimal regions. The additional computational cost depends onhow frequently the hyperparameters are trained, but the tested scheme adds only30% computational overhead regardless of the model size. Since the method issignificantly less computationally demanding compared to similar gradient-basedapproaches to hyperparameter optimization, and consistently finds goodhyperparameter values, it can be a useful tool for training neural networkmodels.

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters