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- W4295093779 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." @default.
- W4295093779 created "2022-09-10" @default.
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- W4295093779 date "2015-02-11" @default.
- W4295093779 modified "2023-09-24" @default.
- W4295093779 title "Gradient-based Hyperparameter Optimization through Reversible Learning" @default.
- W4295093779 doi "https://doi.org/10.48550/arxiv.1502.03492" @default.
- W4295093779 hasPublicationYear "2015" @default.
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