Analysis & theory
Hypergradient
Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
ICML 2015 · first public 2015-02-11 · arXiv 1502.03492
In one paragraph
Computes exact gradients of validation performance with respect to thousands of hyperparameters — including a data-augmentation network whose weights are treated as hyperparameters — by exactly reversing SGD-with-momentum dynamics, a hypergradient technique later reused to differentiate through the training procedure in bi-level dataset distillation.
Where it sits
- Setting: Image classification
Abstract (verbatim from arXiv)
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.
BibTeX (generated; prefer the venue's official entry)
@article{maclaurin2015gradient,
title = {Gradient-based Hyperparameter Optimization through Reversible Learning},
author = {Dougal Maclaurin and David Duvenaud and Ryan P. Adams},
journal = {ICML 2015},
year = {2015}
}