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arXiv · 2008.07277

Adaptive Hierarchical Hyper-gradient Descent

Renlong Jie·Junbin Gao·Andrey Vasnev·Minh-Ngoc Tran
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Abstract

In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures. Meanwhile, we show the relationship between regularizing over-parameterized learning rates and building combinations of adaptive learning rates at different levels. The experiments on several network architectures, including feed-forward networks, LeNet-5 and ResNet-18/34, show that the proposed multi-level adaptive approach can outperform baseline adaptive methods in a variety of circumstances.

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Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran. 2020-08-17. Adaptive Hierarchical Hyper-gradient Descent. https://arxiv.org/abs/2008.07277

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