Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions
(2022)
Journal Article
Zhou, Y., Huang, K., Cheng, C., Wang, X., Hussain, A., & Liu, X. (2023). Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(2), 565-577. https://doi.org/10.1109/tetci.2022.3171797
The training process for deep learning and pattern recognition normally involves the use of convex and strongly convex optimization algorithms such as AdaBelief and SAdam to handle lots of “uninformative” samples that should be ignored, thus incurrin... Read More about Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions.