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Title: A modified Adam algorithm for deep neural network optimization
Authors: Mohamed, Reyad
Amany M., Sarhan
M., Arafa
Issue Date: 2023
Publisher: Springer
Abstract: Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully used in thousands of applications in a variety of fields. Based on these large datasets, they are trained to learn the relationships between various variables. The adaptive moment estimation (Adam) algorithm, a highly efficient adaptive optimization algorithm, is widely used as a learning algorithm in various fields for training DNN models. However, it needs to improve its generalization performance, especially when training with large-scale datasets. Therefore, in this paper, we propose HN Adam, a modified version of the Adam Algorithm, to improve its accuracy and convergence speed. The HN_Adam algorithm is modified by automatically adjusting the step size of the parameter updates over the training epochs.
Description: CC BY
URI: https://link.springer.com/article/10.1007/s00521-023-08568-z
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8343
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