Learning Rate Schedules and Optimizers, A Game Changer for Deep Neural Networks
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-59711-4_28
Reference23 articles.
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2. Jepkoech, J., Mugo, D.M., Kenduiywo, B.K., Too, E.C: The effect of adaptive learning rate on the accuracy of neural networks. Int. J. Adv. Comput. Sci. Appl. 12, 736–751 (2021)
3. Wu, Y., Liu, L.: Selecting and composing learning rate policies for deep neural networks. ACM Trans. Intell. Syst. Technol. 14 (2023)
4. Nakkiran, P.: Learning rate annealing can provably help generalization, Even for Convex Problems (2020)
5. Iiduka, H.: Appropriate learning rates of adaptive learning rate optimization algorithms for training deep neural networks. IEEE Trans. Cybern. 52, 13250–13261 (2022)
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