A second-order-like optimizer with adaptive gradient scaling for deep learning

• URL: http://arxiv.org/abs/2410.05871v1
• Date: Tue, 8 Oct 2024 09:58:38 GMT
• Title: A second-order-like optimizer with adaptive gradient scaling for deep learning
• Authors: Jérôme Bolte, Ryan Boustany, Edouard Pauwels, Andrei Purica,
• Abstract summary: INNAprop is an optimization algorithm that combines the INNA method with the RMSprop adaptive gradient scaling. On image classification (CIFAR-10, ImageNet) and language modeling (GPT-2), INNAprop consistently matches or outperforms AdamW both in training speed and accuracy.
• Score: 13.174512123890016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this empirical article, we introduce INNAprop, an optimization algorithm that combines the INNA method with the RMSprop adaptive gradient scaling. It leverages second-order information and rescaling while keeping the memory requirements of standard DL methods as AdamW or SGD with momentum.After having recalled our geometrical motivations, we provide quite extensive experiments. On image classification (CIFAR-10, ImageNet) and language modeling (GPT-2), INNAprop consistently matches or outperforms AdamW both in training speed and accuracy, with minimal hyperparameter tuning in large-scale settings. Our code is publicly available at \url{https://github.com/innaprop/innaprop}.

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