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Anderson-type acceleration method for Deep Neural Network optimization

Year: 2025

Type: preprint

Abstract: In this paper we consider the neural network optimization. We develop Anderson-type acceleration method for the stochastic gradient decent method and it improves the network permanence very much. We demonstrate the applicability of the method for Deep Neural Network (DNN) and Convolution Neural Network (CNN).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2510.20254

Authors: Ito, Kazufumi, Xue, Tiancheng

Topic: Stochastic Gradient Optimization Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4416618404

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