Gradient-based Hyperparameter Optimization through Reversible Learning

Dougal Maclaurin; David Duvenaud; Ryan P. Adams

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arXiv · 1502.03492

Gradient-based Hyperparameter Optimization through Reversible Learning

Dougal Maclaurin·David Duvenaud·Ryan P. Adams

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Abstract

Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure. These gradients allow us to optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural network architectures. We compute hyperparameter gradients by exactly reversing the dynamics of stochastic gradient descent with momentum.

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Dougal Maclaurin, David Duvenaud, Ryan P. Adams. 2015-02-11. Gradient-based Hyperparameter Optimization through Reversible Learning. https://arxiv.org/abs/1502.03492

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