A Zeroth-Order Adaptive Learning Rate Method to Reduce Cost of Hyperparameter Tuning for Deep Learning
Due to powerful data representation ability, deep learning has dramatically improved the state-of-the-art in many practical applications. However, the utility highly depends on fine-tuning of hyper-parameters, including learning rate, batch size, and network initialization. Although many first-order...
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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
MDPI AG
2021
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| Accès en ligne: | https://doaj.org/article/fea104c85f094c74b56e338e92eeb8ae |
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