基于熵的光学相干断层成像深度神经网络训练优化

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Artificial Intelligence Pub Date : 2024-05-24 DOI:10.1080/08839514.2024.2355760

Karri Karthik, Manjunatha Mahadevappa

引用次数: 0

摘要

本文针对深度学习模型所需的训练历元数提出了一种优化技术。所提出的方法无需单独的验证数据和大量的...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Entropy-based deep neural network training optimization for optical coherence tomography imaging

This paper presents an optimization technique for the number of training epochs needed for deep learning models. The proposed method eliminates the need for separate validation data and significant...

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来源期刊

Applied Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

5.20

自引率

3.60%

发文量

106

审稿时长

6 months

期刊介绍： Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.