AdaFamily: A family of Adam-like adaptive gradient methods

Autor:	Fassold, Hannes
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	We propose AdaFamily, a novel method for training deep neural networks. It is a family of adaptive gradient methods and can be interpreted as sort of a blend of the optimization algorithms Adam, AdaBelief and AdaMomentum. We perform experiments on standard datasets for image classification, demonstrating that our proposed method outperforms these algorithms. Comment: submitted for ISPR 2022 conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.01603 Zobrazit plný text záznamu View this record from Arxiv